Deliverable D4.1. State-of-the-art concerning texture influence on skid resistance, noise emission and rolling resistance

Transcription

1 ROlling resistance, Skid resistance, ANd Noise Emission measurement standards for road surfaces Collaborative Project FP7-SST-2013-RTD-1 Seventh Framework Programme Theme SST : Innovative, cost-effective construction and maintenance for safer, greener and climate resilient roads Start date: 1 November 2013 Duration: 36 months Deliverable D4.1 State-of-the-art concerning texture influence on skid The research leading to these results has received funding from the European Community s Seventh Framework Programme (FP7/ ) under grant agreement n Main Editor(s) Luc Goubert Due Date 30/04/2014 Delivery Date 30/04/2014 Work Package Dissemination level WP4 Common issues: Texture influence, reference tyres and reference surfaces Public Project Coordinator Manfred Haider, AIT Austrian Institute of Technology GmbH, Giefinggasse 2, 1210 Vienna, Austria. Tel: +43(0) , Fax: +43(0) manfred.haider@ait.ac.at. Website:

2 Contributor(s) Main Contributor(s) Luc Goubert, Belgian Road Research Centre (BRRC), , Min Tan Do, Institut français des sciences et technologies des transports (IFSTTAR), , Anneleen Bergiers, Belgian Road Research Centre (BRRC), , Rune Karlsson, Swedish National Road and Transport Research Institute, , Ulf Sandberg, Swedish National Road and Transport Research Institute, , Contributor(s) (alphabetical order) Johan Maeck, Belgian Road Research Centre (BRRC), , Review Reviewer(s) 1. Jørgen Kragh, DRD 2. Martin Greene, TRL The input from the transnational MIRIAM project 1 and the Scandinavian NordTex project 2 are gratefully acknowledged Date: 30/04/2014 Version: (122)

3 Control Sheet Version History Version Date Editor Summary of Modifications /03/2014 Luc Goubert First version /04/2014 Luc Goubert Comments integrated, final version Final Version released by Circulated to Name Date Recipient Date Manfred Haider 30/04/2014 Coordinator Consortium European Commission Date: 30/04/2014 Version: (122)

4 Table of Contents 1 Introduction Texture and road unevenness ranges Commonly used measures describing the road surface Positive and negative textures (skewness) Tyre tread enveloping of texture The influence of texture on noise Tyre/road noise generation mechanisms Tyre vibrations Air pumping The horn effect Sound absorption Synthesis Modelling of tyre/road noise Introduction: why modelling? Statistical models Analytical models Hybrid models The influence of macro- and megatexture on rolling resistance Background Definitions Texture levels Measurements in Sweden Measurements in Belgium Pilot study MIRIAM Measurements in Minnesota Overview Macrotexture and megatexture levels Measurements in Sweden Pilot study MIRIAM Date: 30/04/2014 Version: (122)

5 3.4.3 Overview Mean Profile Depth Measurements in France Measurements in Belgium Measurements in New Zealand Measurements in Poland Measurements in Sweden and Denmark Pilot study MIRIAM German study truck trailer Measurements in Minnesota Measurements in the Netherlands Overview Root Mean Square (rms) macro- and megatexture Measurements in Germany Measurements in UK Measurements in the Netherlands Measurements in Minnesota Overview Skewness Pilot study MIRIAM Measurements in Sweden Measurements in Minnesota Measurements in the Netherlands Summary for the influence of macro- and megatexture on rolling resistance The influence of the unevenness on the rolling resistance Introduction Measures for road unevenness Methods for estimating the unevenness contribution to the rolling resistance In general Force measurements Date: 30/04/2014 Version: (122)

6 4.3.3 Simulation A general RR model for dry road conditions Some results and experiences from previous coast down studies Road surface texture and skid resistance Background Definitions Tyre/road interface Role of road surface texture Macrotexture and skid resistance Current state of knowledge Other characterization methods Microtexture and skid resistance Understanding the physical phenomena Microtexture measurement Microtexture characterization Can we predict skid resistance from microtexture? Conclusions for skid resistance Conclusion Date: 30/04/2014 Version: (122)

7 Abbreviations Abbreviation AAV4 AOT ASTM BASt BRRC DEUFRAKO ECRPD ERNL ETD FC HFC HyRoNE IFSTTAR INRETS IRI ISO MnDOT MnROAD MIRIAM MPD MTD NAASRA OBSI PCC PFF PIARC Meaning Avon AV4 (tyre) Acoustic Optimization Tool American Society for Testing Materials Bundesanstalt für Strassenwesen Belgian Road Research Centre Deutsch-Französische Kooperation in der Verkehrsforschung Energy Conservation in Road Pavement Design (project) Estimated Road Noisiness Level Estimated Texture Depth fuel consumption Highway Friction Coefficient Hybrid Road Noise Emission model l'institut français des sciences et technologies des transports Institut National de REcherche sur les Transports International Roughness Index International Standardization Organization Minnesota Department of Transportation Minnesota Road Research Project Models for rolling resistance In Road Infrastructure Asset Management Systems (project) Mean Profile Depth Mean Texture Depth National Association of Australian State Road Authorities On Board Intensity Measurement Portland Cement Concrete Prüfstand Fahrzeug/Fahrbahn World Road Organization Date: 30/04/2014 Version: (122)

8 RMS RST vehicle RRC Root Mean Square Road Surface Testing vehicle Rolling Resistance Coefficient R² trailer Rolling Resistance trailer SD SPERoN SRTT TNO TRIAS TUG Segment Depth Statistical Physical Explanation of Rolling Noise (model) Standard Reference Test Tyre Netherlands Organisation for Applied Scientific Research Tyre-Road Interaction Acoustic Simulator Technical University Gdansk Date: 30/04/2014 Version: (122)

9 List of Figures Figure 1: Illustration of the various scales of road roughness and their relation to the road/tyre/vehicle interaction Figure 2: Ranges in terms of texture wavelength and spatial frequency of texture and unevenness and their most significant, anticipated effects; from [Sandberg and Ejsmont, 2002] Figure 3 Illustration of the terms Segment, Baseline, Segment Depth (SD), and Mean Segment Depth (MSD) (SD and MSD are expressed in millimetres) Figure 4: Example of one-third-octave band texture spectrum with indication also of the texture levels of the octave bands L TX500 and L TX Figure 5: Examples of surface profiles of positive macrotexture (left) and negative macrotexture (right). Skewness of the left profile would be positive (somewhat > 0) while it would be substantially negative for the right profile (<< 0) Figure 6 Flow chart showing the iterative procedure to obtain the enveloped profile with the von Meier method (after [von Meier, 1992], with two corrections) Figure 7: A 100 mm long profile measured on porous asphalt 0/6 on the IFSTTAR test track in Nantes processed (enveloped) by the method by van Meier et al, using three selected constants representing tyre stiffness Figure 8: Third-octave-band texture spectrum of the original profile of the porous asphalt 0/6 IFSTTAR test track in Nantes, compared to the spectra representing the three enveloped curves. These spectra are measured and calculated over a length of 210 m. One can see that the main action of the enveloping is suppression of the amplitudes at the shorter texture wavelengths, due to a smoothening effect Figure 9 Noise due to tyre vibrations: impact of tyre tread on irregularities causes the tyre tread to vibrate Figure 10 Noise due to tyre vibrations: the side walls of the tyre start to vibrate as well as they are coupled with the vibrating tyre tread Figure 11 Noise due to air pumping: compression of air at the leading edge tyre/road contact zone 27 Figure 12 Noise due to air pumping: suction of air at the rear end of the tyre/road contact zone Figure 13 Sound amplification by the horn effect Figure 14 Sound absorption coefficient vs. frequency. The closer the coefficient to 1, the higher the sound absorption capacity of the road surface in the concerned frequency range. In the example presented, sound absorption is high at frequencies of about 550; 1,800; 3,000 and 4000 Hz Figure 15 Correlation between the noise emission spectrum and the texture spectrum as found by Sandberg and Descornet [Sandberg, 1980] Date: 30/04/2014 Version: (122)

10 Figure 16 Sound pressure levels predicted with the TRIAS model versus measured values for three sets of tyres on several road surfaces. The solid line indicates a 1 to 1 relation and the dashed line ist he linear regression line Figure 17 Comparison of a measured SPB sound spectrum (solid blue line) with the AOT prediction (gray dashed line) for a low noise asphalt concrete [Oddershede 2012] Figure 18 Comparison of a measured CPX sound spectrum (solid blue line) with the AOT prediction (gray dashed line) for a low noise asphalt concrete [Oddershede 2012] Figure 19 Validation of SPERoN model for eight surfaces made in the French-German project DEUFRAKO [Auerbach, 2009] Figure 20 Validation of HyRoNE model for a large number of surfaces made in the French-German project DEUFRAKO [Auerbach, 2009] Figure 21: Correlation between fuel consumption per km and road roughness/texture level as a function of texture wavelength [Sandberg, 1990] Figure 22: Correlation between RRC and road roughness/texture level as a function of texture wavelength [Descornet, 1990]. The dashed line is the result when the six (concrete) pavements that had transverse textures (out of 37 pavements) were neglected Figure 23: Comparison of RRC - texture spectra correlations obtained in the Artesis-BRRC project with previous research Figure 24: Correlations at various texture wavelengths and tyres for measurements performed at 50 and 80 km/h without applying the enveloping procedure to the texture Figure 25: Correlations at various texture wavelengths and tyres for measurements performed at 50 and 80 km/h with applying the enveloping procedure to the texture Figure 26: Relation between fuel consumption (FC) at 60 km/h and macrotexture level LMA in the mm texture wavelength range. Diagram scanned from [Sandberg, 1990] Figure 27: Summarizing graph correlations MPD, LMa and LMe for all tyres and speeds without enveloping [Bergiers, 2011] Figure 28: Summarizing graph correlations MPD, LMa and LMe for all tyres and speeds with enveloping [Bergiers, 2011] Figure 29: Average values and standard deviations LMa, LMe per category (overall, speed, institute, tyre) without enveloping [Bergiers, 2011] Figure 30: Average values and standard deviations LMa, LMe per category (overall, speed, institute, tyre) with enveloping [Bergiers, 2011] Figure 31: RRC versus texture depth [Descornet, 1990] Figure 32: Results of Swedish-Polish studies of the relation between RRC and macrotexture, for car tyres, where macrotexture is represented by the MPD. A certain cluster of points and associated Date: 30/04/2014 Version: (122)

11 regression line belong to the same data set, measured within a few days, whereas the data sets have been collected in The data in grey and black are from measurements in Other colors are from 2009 [Sandberg et al., 2011] Figure 33: RRC versus MPD for a measurement series in Denmark, where the red symbol shows the result for a porous asphalt with maximum 8 mm chippings [Sandberg et al., 2011] Figure 34: Average values and standard deviations MPD per category (overall, speed, institute, tyre) with and without enveloping Figure 35: Correlation between MPD [mm] and RRC [ ] [Bode, 2013] Figure 36: Rolling resistance coefficient (expressed in kg/t) versus MPD for all road sections. Different colours represent various road surface types. Open circles indicate road sections older than 8 years [Hooghwerff, 2013] Figure 37: Relation between RRC (probably average of four car tyres) and rms value of "coarse texture" for the 10 tested road sections. Texture values are given as a proportion of the texture of one of the surfaces (the rightmost data point, which is set as 1.0 mm). [Sander, 1996] Figure 38: Relation between RRC for the four car tyres A10-A13 and rms value of the three texture ranges for the 10 tested road sections. FT = fine texture, GT = coarse texture and MT = megatexture. [Sander, 1996] Figure 39: Average RRC values from drum tests - two runs for each of four tyres at three different speeds (50/90/120 km/h) on 10 of the 11 drum surfaces [Sander, 1996] Figure 40: Relation of rolling resistance to fine macrotexture ( mm) for drum tests (using tyre 175/70 R13T) [Sander, 1996] Figure 41: The special geometrically patterned surfaces produced for the tests. From left to right: hemispheres cubes tetrahedral discs [Parry, 1998] Figure 42: Rolling resistance coefficient for the three tyres PIARC (smooth), Tyre A and Tyre B, versus texture described by the contact part of the profile rms value; i.e. rmsc. [Parry, 1998] Figure 43: Rms per measurement run [Hooghwerff, 2013] Figure 44: Skewness versus MPD for the IFSTTAR test tracks in Nantes, France [Sandberg et al., 2011] Figure 45: Results of the rolling resistance measurements comparing the polished surface with the unpolished surface, fort he three tyres and two speeds [Sandberg et al., 2011] Figure 46: Skewness per measurement run [Hooghwerff, 2013] Figure 47 Additional driving resistance for a test vehicle, a car, calculated by VETO (Hammarström at al., 2008) Figure 48 Tyre/wet road contact area Figure 49 Hysteresis and adhesion components of tyre/road friction [Hall et al., 2009] Date: 30/04/2014 Version: (122)

12 Figure 50 Road surface texture scales [Sandberg, 1998] Figure 51 Cartography of a road surface (80 mm 80 mm) Figure 52 Variation of friction coefficient with speed and the influence range of road surface macro and microtexture [Sandberg, 1998] Figure 53 Definition of the Mean Profile Depth (MPD) [Sandberg, 1998] Figure 54 Definition of the Mean Texture Depth (MTD) [ISO, 2006] Figure 55 Variation of friction coefficient with speed for various macro/microtexture types [Hall et al., 2009] 87 Figure 56 Variation of friction coefficient with speed (a: graph obtained from braking tests (Gothié et al., 2001); b: Stribeck curve representing boundary (BL), mixed (ML) and elastohydrodynamic (EHL) lubrication regimes [Faraon, 2005]) Figure 57 Variation of friction coefficient with water film thickness [Hall et al., 2009] Figure 58 Peak profile height Rp (a) and Mean peak profile height Rpm (b) [Zygo, 1993] Figure 59 Surface bearing (a: bearing area; b: bearing area curve) Figure 60 Characterization of Abbott curve and corresponding parts of a profile [Stout et al, 1993]. 92 Figure 61 Rough surfaces and related bearing area curves [Stout et al, 1993] Figure 62 Generation of hydrodynamic pressure due to relative slip between the tyre and the road [Moore, 1975] Figure 63 Variation water depth with slip speed [Moore, 1975a] Figure 64 Influence of roughness and shape on the time of sinkage [Taneerananon and Yandell, 1981] Figure 65 Contact of steel sliders on wet rubber (a: calculated contact pressure; b: variation of measured friction coefficient with calculated contact pressure) [Sabey, 1958] Figure 66 Variation of friction coefficient with water depth (a: sandblasting of the specimen surface ; b : comparison with/without microtexture) [Do et al., 2013] Figure 67 Variation of friction coefficietn with elastohydrodynamic number [Moore, 1975b] Figure 68 Macro- and microtexture parameters (letters A to F) defined by [Schonfeld, 1970] Figure 69 Example of marks attributed to microtexture of coarse aggregates (Holt and Musgrove, 1982) Figure 70 InfiniteFocus sensor (a: view of the equipment; b: principle of focus variation [Danzl et al., 2011]) Figure 71 Example of height cartography of aggregate mosaic surface measured by InfiniteFocus sensor Date: 30/04/2014 Version: (122)

13 Figure 72 Image-based system (a: view of the prototype; b: examples of road surfaces (left) and the height cartography extracted from images (right)) [Ben Slimane et al., 2007] Figure 73 Variation of friction coefficient with microtexture for dry and wet surfaces (Do et al., 2013) Figure 74 Characterization of microtexture [Forster, 1981] Figure 75 Correlation between microtexture parameters (a: height; b: shape) and friction coefficient measured by British Pendulum [Forster, 1981] Figure 76 Definition of motif Figure 77 Surfaces used for the assessment of microtexture parameters [Zahouani et al., 2000] Figure 78 Correlation between motif descriptors and friction (friction values were multiplied by 100 on the Y axis) Figure 79 Definition of indentor and its characteristics [Do et al., 2000] Figure 80 Correlation between indentor parameters and friction coefficient (a: relief; b: shape) [Do et al., 2000] Figure 81 Equilibrium of a solid on an inclined surface Figure 82 Rubber pad sliding on indentor Figure 83 Validation of the mechanical model on mosaics of aggregates [Do, 2004] Figure 84 Analysis of the microtexture (a: extraction of a microtexture profile the red curve obtained by moving average method represents the aggregate form; b: simulation of the masking effect of water) [Do et al., 2013] Figure 85 Comparison of predicted and measured friction coefficient [Do et al., 2013] Date: 30/04/2014 Version: (122)

14 List of Tables Table 1 Various mechanisms involved in the production and propagation of tyre/road contact noise Table 2: Best estimations of coefficients for the basic rolling resistance (Cr) and the most important variables in the model equation, describing the contributions of the parameters to the rolling resistance coefficient. Data from [Karlsson et al., 2011] Table 3: The correlation coefficients between rolling resistance and texture in the three bands, for both the drum and the trailer measurements Table 4: Average rolling resistance coefficient values (temperature corrected) in %, measured on the drum and compared with trailer measurements on a road surface Table 5 Estimated parameter values based on measurements and VETO simulations.* Table 6: Vehicles used for coast down measurements in several projects VTI participated Date: 30/04/2014 Version: (122)

15 Executive Summary The main objective of the ROSANNE project is to advance the harmonization of measurement methods for skid of road pavements and prepare for standardization. Research in the past has shown that these three parameters are heavily influenced by the texture of the road surface, albeit that the relevant texture ranges differ from parameter to parameter. This deliverable is a key document describing the state of the art of the knowledge about the relationship between the texture on the one hand and the three mentioned parameters on the other hand. One of the reasons why these relationships are so important is that if one could predict the noise, skid resistance and the rolling resistance from the texture, one could monitor the road network or assess the conformity of production of a newly constructed pavement by simply measuring the texture. Texture in the macro 3 -, mega 4 - and unevenness 5 range can be measured in a fast (at high speed), reliable and hence relatively cheap way, compared to the direct measurement of noisiness, skid resistance and rolling resistance. Measuring microtexture 6 playing an important role for skid resistance at traffic speed is not yet possible. The document starts with an introductory chapter about texture, explaining the texture ranges, the relevant standards, the most common texture indicators (like the Mean Profile Depth) and texture spectra. The important phenomenon, known as tyre enveloping is described. Tyre enveloping is the phenomenon that a tyre feels only the upper part of the texture, due to the fact that it is not infinitely soft and that the tyre rubber does not touch the bottom of narrow and deep depressions in the road surface. Tyre enveloping appears to be important for the relation between texture and noise and rolling resistance. The second chapter describes the extensive knowledge which has been acquired since the late 1970ties on the relation between texture and tyre/road noise. The phenomena contributing to the tyre road noise are described and an overview is given of the models which have been developed to predict the tyre/road noise based on macro- en megatexture. Although lots of time and money has already been spent on this type of research, no model appears yet to exist capable of describing the noisiness of a pavement with an acceptable precision. Chapter 3 describes the efforts trying to link the rolling resistance (of mainly car tyres) to the macroand megatexture and it appears that the rolling resistance is very clearly linked to the Mean Profile Depth, which is promising for the possibility to assess rolling resistance with a texture measurement. Chapter 4 describes the influence of the unevenness on the rolling resistance, which is treated separately as the energy dissipation is in this case mainly in the car suspension, and not in the tyres as with the rolling resistance caused by macro- and megatexture. 3 Irregularities with dimensions between 0,5 mm and 50 mm 4 Irregularities with dimensions between 50 mm and 0,5 m 5 Irregularities with dimensions between 0,5 m and 50 m 6 Irregularities with dimensions below 0,5 mm Date: 30/04/2014 Version: (122)

16 Chapter 5 describes the complicated relation between texture (macro- and microtexture) and the skid resistance. State of the art models are capable of predicting skid resistance from microtexture with a reasonable precision, but more research is needed on the precise relation between micro- and macrotexture, water depth and skid resistance. Date: 30/04/2014 Version: (122)

17 1 Introduction The main objective of this project is to advance the harmonization of measurement methods for skid of road pavements and prepare for standardization. These three parameters are heavily influenced if not totally determined by the road texture. A good understanding of the state of the art of the knowledge of the relation between the three mentioned parameters and the texture is necessary. This document yields a comprehensive overview. 1.1 Texture and road unevenness ranges The basis for the description of the road roughness (texture and unevenness) is the profile of the surface along lines (in this case) representing the rolling paths of vehicle tyres. The profile of the surface is described by two coordinates: one in the surface plane, called distance (the abscissa), and the other in a direction normal to the surface plane, called vertical displacement (the ordinate). The distance may be in the longitudinal or lateral (transverse) directions in relation to the travel direction on a pavement, or any direction between these extremes; although for tyre/road noise, the longitudinal profile is the most important one. Texture wavelength" is a descriptor of the wavelength components of the profile and is related to the concept of the Fourier Transform of a time series. The profile may be studied in more or less detail, and the features of these will have different influences on the road/tyre/vehicle interaction. Figure 1 illustrates this. Figure 1: Illustration of the various scales of road roughness and their relation to the road/tyre/vehicle interaction. Date: 30/04/2014 Version: (122)

18 As appears later, the macro- and megatexture are road surface properties that have major influences on tyre/road noise; macro- and microtexture on skid resistance and unevenness, mega- and macrotexture on rolling resistance. Therefore it is justified to examine these associated terms a little closer. The following is an adaptation from ISO :2009. Texture, or pavement texture, is the deviation of a pavement surface from a true planar surface, with a texture wavelength less than 0.5 m. It is divided into the sub-ranges micro-, macro- and megatexture; see Figure 2. Figure 2: Ranges in terms of texture wavelength and spatial frequency of texture and unevenness and their most significant, anticipated effects; from [Sandberg and Ejsmont, 2002]. Microtexture is the deviation of a pavement surface from a true planar surface with the characteristic dimensions along the surface of less than 0.5 mm, corresponding to texture wavelengths up to 0.5 mm expressed as one-third-octave centre wavelengths. Macrotexture is the deviation of a pavement surface from a true planar surface with the characteristic dimensions along the surface of 0.5 mm to 50 mm, corresponding to texture wavelengths with one-third-octave bands including the range 0.63 mm to 50 mm of centre wavelengths. Megatexture is the deviation of a pavement surface from a true planar surface with the characteristic dimensions along the surface of 50 mm to 500 mm, corresponding to texture wavelengths with onethird-octave bands including the range 63 mm to 500 mm of centre wavelengths. Date: 30/04/2014 Version: (122)

19 Unevenness is the deviation of a pavement surface from a true planar surface with the characteristic dimensions along the surface of 0.5 m to 50 m, corresponding to wavelengths with one-third-octave bands including the range 0.63 m to 50 m of centre wavelengths. 1.2 Commonly used measures describing the road surface A common way to quantify texture and unevenness is to filter the profile curve through different bandpass filters having passbands corresponding to the texture wavelengths shown in Figure 3.2 and defined in the text above and then to measure the rms (root-mean-square) output value of the filtered profile curve, using the unit [mm]. The measures in the various ranges may be distinguished by using the symbol a Mi for micro-texture, a Ma for macrotexture and a Me for megatexture, with values expressed in mm rms. The symbol "a" denotes "amplitude". However, it has been preferred in especially noise-related studies to calculate and use the logarithms of these linear measures, then labelled L Mi, L Ma and L Me, expressed in db relative to 1 μm rms. One advantage of this is that in most practical studies, this will result in a statistical distribution of the values which is more normal (Gaussian) than when using the corresponding linear measures. Thus, here we have the following measures: Microtexture level, L Mi Macrotexture level, L Ma Megatexture level, L Me For the unevenness range, there is no special symbol commonly used, corresponding to a Ma and a Me and the logarithm conversion is seldom used. For the very commonly used ranges macrotexture and unevenness, special measures have been standardised and are commonly used. For macrotexture we have two measures which are commonly used: the Mean Texture Depth (MTD) and the Mean Profile Depth (MPD). MTD is a measure developed in the middle of the 20th century, where a certain volume of sand (later replaced by glass spheres of mm diameter) is spread out with a tool (a rubber pad, often an ice hockey puck) flush with the peaks in the surface into a circular patch on the road surface, the diameter of which is measured. From the patch diameter and the sand volume, the mean depth of the texture over this patch is calculated. This is called the "volumetric patch method", earlier known as the "sand patch method". This method is described in [EN ]. MPD is a measure developed in the 1980's and 1990's intended to replace the MTD, which could be measured by moving vehicles using lasers and laser sensors to record the profile curve, from which a two-dimensional representative of the three-dimensional patch may be calculated. The corresponding standard, ISO , is currently being revised, and the new calculation procedure is illustrated in Figure 3. From two halves of a 100 mm long profile (two 50 mm long segments), the so-called Mean Segment Depth (MSD) is calculated. By averaging several such MSD values over a certain road section, the MPD is obtained. The actual calculation is more complex than one would think while reading this description, so the ISO should be consulted if actual measurements are planned. Date: 30/04/2014 Version: (122)

20 Figure 3 Illustration of the terms Segment, Baseline, Segment Depth (SD), and Mean Segment Depth (MSD) (SD and MSD are expressed in millimetres). Sometimes, the term Estimated Texture Depth (ETD) is seen. This is an estimation of the MTD from a measurement of the MPD, with a conversion equation appearing in ISO In the unevenness range, a special measure is the International Roughness Index (IRI). It is calculated using a quarter-car vehicle mathematical model, supposed to be driven at 80 km/h (50 mph), whose response is accumulated to yield a roughness index which is the accumulated slope of the profile curve per km of road, be it negative or positive, expressed in mm/km or m/km. Since its introduction in the 1980's IRI has become the road unevenness index most commonly used worldwide for evaluating and managing road systems. IRI is specified in the international standard ASTM E and in the CEN standard [EN ]. In analogy with the macro- and megatexture levels mentioned above, one may filter the profile curve with narrower filters and calculate "spectral levels" in the corresponding pass-bands. The most common bandpass filters are one-third-octave bands. By using such filters one obtains a texture spectrum. A typical texture spectrum (in one-third-octave bands) is shown in Figure 4, also including two special octave band levels. Date: 30/04/2014 Version: (122)

21 Figure 4: Example of one-third-octave band texture spectrum with indication also of the texture levels of the octave bands L TX500 and L TX63. The level of each of the two octave bands is indicated by the level of the top line of each rectangle. Note also that the presented spectrum represents a pavement having a relatively low megatexture; in this case a dense asphalt concrete with maximum 10 mm chippings, in nearly new condition. 1.3 Positive and negative textures (skewness) A possible asymmetry of the profile, see Figure 5, should potentially have significant influence on the rolling resistance. A 'positive' texture (exhibiting protrusions) should show a significantly different behaviour in functional qualities, like skid resistance or noise generation, than a negative texture (exhibiting depressions). To quantify such asymmetry, one may apply an analysis of the skewness, i.e. the third statistical moment of the quantity, to reveal this aspect of the profile. Skewness of the profile, rsk, is defined in ISO as the quotient of the mean cube value of the ordinate values Z(x) and the cube of the rms value, within an evaluation length l, according to the equation: Skewness is dimensionless. Skewness (or just "skew") is a measure of assymmetry of the amplitude distribution (in this case of the ordinate values). This indicates whether the profile curve exhibits a Date: 30/04/2014 Version: (122)

22 majority of peaks directed upward (positive skew) or downward (negative skew). For a normal distribution rsk is zero. Figure 5: Examples of surface profiles of positive macrotexture (left) and negative macrotexture (right). Skewness of the left profile would be positive (somewhat > 0) while it would be substantially negative for the right profile (<< 0). 1.4 Tyre tread enveloping of texture When a tyre runs on a textured road surface, it does not necessarily make contact with all points on the surface in its wheel path. This is, e.g., the case when the texture shows deep and irregular valleys (such as on porous asphalt) or deep and relatively regular grooves" (such as on transversally grooved concrete). The tyre is said to be "enveloping" the part of the surface with which it is in contact. It has been known already since the beginning of the 1990's that the fact that a tyre envelops only part of the surface of the pavement plays an important role for the prediction of tyre/road noise. As it is related with the way how the road texture deforms the tyre rolling over it, it should also be important for the aspect of rolling resistance. More or less complex ways of modelling the tyre enveloping of road surface textures have been developed and tried in various projects. Such models may be used to process the texture profile before calculating the texture spectrum or other texture parameters. During this process, called enveloping, the points on the profile which are not in contact with the tyre because they lie too deep are discarded and replaced by a point with a higher amplitude, supposed to be in contact with the tyre tread. The enveloping procedure requires tyre property data, e.g. Young's modulus, and Poisson coefficient, in order to determine how well the tyre tread may envelop the texture, from which a mathematical function or operation can be applied to the profile curve with the effect of cutting away the non-contact points. The ideal is to produce a new curve which follows the tyre tread deflection. There are several ways of doing this, of which the most commonly used ones are: A mathematical/empirical method proposed by von Meier et al. [von Meier 1992] A tyre-physics-based method originally proposed by Clapp [Clapp, 1984], later improved by Clapp et al [Clapp et al. 1988] Clapp et al's method improved by Fong [Fong, 1998] Clapp et al's method improved by Klein and Hamet [Klein and Hamet, 2004]. Date: 30/04/2014 Version: (122)

23 The method by von Meier et al is not based on a physical model but is an empirical operation based on the mathematical limitation of the second-order derivative. For a discrete texture profile, one can express this as follows: (z i (z i-1 + z i+1 )/2) / x² d* where z i is the amplitude of the i-th point of the profile, x the sampling step and d* the value to which the second derivative of the enveloped profile will be limited. The parameter d* is a measure of the softness of the tyre. A value for d* representing the average stiffness of car tyres is proposed to be m -1. A flow diagram showing the iterative calculation procedure is shown in Figure 6 [von Meier, 1992]. It should be noted that two sign errors figuring in the original publication have been corrected. Figure 6 Flow chart showing the iterative procedure to obtain the enveloped profile with the von Meier method (after [von Meier, 1992], with two corrections) Date: 30/04/2014 Version: (122)

24 A 100 mm long sample of a profile measured on the IFSTTAR test track on a porous asphalt 0/6 is shown in Figure 7. Three enveloped profiles calculated with the von Meier method are shown, corresponding to a very soft tyre (d* = 0.1 m -1 ), a stiff tyre (d* = 0,01 m -1 ) and a "medium tyre" (d* = 0,054 m -1 ). Figure 7: A 100 mm long profile measured on porous asphalt 0/6 on the IFSTTAR test track in Nantes processed (enveloped) by the method by von Meier et al., using three selected constants representing tyre stiffness. The texture spectra of the corresponding profile curves are shown in Figure 8. Date: 30/04/2014 Version: (122)

25 Figure 8: Third-octave-band texture spectrum of the original profile of the porous asphalt 0/6 IFSTTAR test track in Nantes, compared to the spectra representing the three enveloped curves. These spectra are measured and calculated over a length of 210 m. One can see that the main action of the enveloping is suppression of the amplitudes at the shorter texture wavelengths, due to a smoothening effect. In contrast to van Meier's method, Clapp s envelopment procedure is based on a physical model. It consists in evaluating the contact between a rigid body (indentor, in this case the textured pavement) and a semi-infinite elastic body (the tyre). The tyre is characterized by means of its Young's modulus and Poisson's ratio. Clapp solves the problem of finding the displacement of the indented elastic body, but does so in a quite approximate way. In the enveloped profile, straight lines are drawn between consecutive peaks over valleys which are too deep to be reached by the tyre rubber. This seems to be a quite rough approximation and not totally realistic, while von Meier's method instead lacks realism in that the rubber rides only on rather small points created by the profile peaks, which should give very high local contact pressures. The Clapp method was later improved by Fong in New Zealand [Fong 1998], although still with straight lines for tyre rubber surfaces without texture contact. Further development of Clapp's method was made by Klein and Hamet at INRETS, introducing the mathematical concept of Green s functions [Klein 2005]. They proposed an iterative algorithm that rapidly converges and yields realistic enveloping curves, which are similar to what is obtained with the von Meier method. The disadvantage of the INRETS method is the complex mathematical calculations needed and it is far from sure that the results are significantly better than those obtained with the simple and fast von Meier method. Further research is needed on this topic. Date: 30/04/2014 Version: (122)

26 2 The influence of texture on noise 2.1 Tyre/road noise generation mechanisms Several mechanisms contribute to the global tyre/road noise emission and the contribution of each mechanism is dependent on tyre and road parameters. There are three road parameters that influence tyre/road noise: Texture Porosity Mechanical impedance Moisture or other pollution on the pavement could be considered as a fourth parameter influencing the tyre/road noise, but this is not considered further here. In this deliverable the main phenomena which have been identified to be at the origin of tyre/road noise, such as vibrations in tyres, air pumping, and the horn effect will be described. The sound absorption phenomenon, although not a real texture phenomenon, is explained as well as there is some connection between the influence of the texture and the porosity. More elaborated descriptions can be found in [Sandberg 2001] and [Sandberg 2010] Tyre vibrations When a tyre rolls on a surface which is not perfectly smooth, the road irregularities will induce vibrations in the tyre tread and indirectly in the walls of the tyre. Sound radiation from these vibrations significantly contributes to the production of tyre/road noise. This factor gains importance with increasing irregularities (within a specific range of horizontal dimensions) of the road surface (see Figure 9 and Figure 10). Figure 9 Noise due to tyre vibrations: impact of tyre tread on irregularities causes the tyre tread to vibrate Figure 10 Noise due to tyre vibrations: the side walls of the tyre start to vibrate as well as they are coupled with the vibrating tyre tread Only irregularities which are deep and wide enough (typically larger than 1 cm in horizontal size) cause vibrations, whereas those smaller than 1 cm cause almost none and are on the contrary Date: 30/04/2014 Version: (122)

27 beneficial for the acoustic performance of the pavement, as they suppress air pumping (see Section 3.2). Tyre vibrations are maximized when the road surface exhibits irregularities with horizontal dimensions around 8 cm approximately, equal to the size of the contact area with the tyre. Setts, blocks or tiles which have these dimensions are intrinsically noisy and should therefore be avoided if one wants to limit the traffic noise. They have been known to generate noise levels that are at least 6 db(a) higher than conventional road surfaces. Vibrations in tyres generate noise in the low frequency range (approximately 80 to 1,250 Hz), depending on the speed and mass of the vehicle. Noise as perceived inside a car is, therefore, greatly affected by the presence of these larger irregularities (see further). Other effects related to the interaction between the tyres and the road surface may take place, but they are minor. The succession of adhesion and release phenomena also referred to as stick-slip between tyre rubber and road aggregates in the contact area generates high-frequency noise (squealing). These phenomena are connected with shocks in tyre tread elements, which may result in the emission of rather high-frequency sounds (above 1,250 Hz) and, in extreme cases, may cause the tyre to squeal in bends. Also worth mentioning is the stick-snap phenomenon, which may produce quite shrill sounds (with frequencies exceeding 1,250 Hz) when the grip of the tyre on the road is too strong (this may occur for example when driving on a freshly laid asphalt pavement) Air pumping A tyre rolling on a smooth surface produces noise as the air trapped between the tyre and the noninterconnected voids is compressed and then suddenly released. The air pumping phenomenon, which dominates sound emission in the high frequency range (approximately 1 to 5 khz), is stronger as the tyre/road contact area increases. It is not observed or greatly reduced on a surface with small asperities (typically smaller than 1 cm in height) or with good porosity, as these allow easier circulation of air (see Figure 11 and Figure 12) Figure 11 Noise due to air pumping: compression of air at the leading edge tyre/road contact zone Date: 30/04/2014 Version: (122)

28 Figure 12 Noise due to air pumping: suction of air at the rear end of the tyre/road contact zone The air present in the tyre tread elements is then able to freely escape between the asperities or the voids at the time of the contact with the road, which reduces the compression effect. Small-sized asperities can generally be obtained by using small aggregates in the surface course (e.g., a small-graded asphalt mixture). They should be homogeneous (avoid accumulations of aggregates), but must not form a regular pattern (no equally spaced grooves), which would produce whining. As a general rule, it is advisable to have sufficiently deep asperities (at least 0.5 mm of texture depth) homogeneously distributed in a dense and small to medium-graded pattern (maximum 10 mm). Porosity is linked with the presence of surface voids that are connected with the voids in the structure of the surface course (minimum 15 to 20 % of voids, e.g. like in porous asphalt) The horn effect This mechanism does not produce noise by itself, but amplifies the noise generated by other phenomena. Sounds can reverberate several times in the air horn (or conical space) formed by the tyre and the road surface, which results in intensifying them (see Figure 13). The principle is similar, for example, to that experienced with a megaphone or a trumpet. The amplification mainly occurs in the most audible frequency range (1 to 3 khz). The effect is produced by successive reflections of sound waves in the conical space formed by the tyre and the road surface, on close-graded road surfaces in the front or at the rear of the wheel. On open-graded or porous pavements, the amplifying effect is at least partly mitigated by sound absorption in the voids. Figure 13 Sound amplification by the horn effect Date: 30/04/2014 Version: (122)

29 2.1.4 Sound absorption Road surfaces have a capability of absorbing sounds which depends greatly on their porosity. Interconnected voids in a surface course absorb not only tyre/road contact noise, but also engine noise. This is particularly of interest at low speeds in urban areas, where engine noise is predominant. To be effective in absorbing noise, the porous surface course must have a minimum thickness of 40 mm and a minimum voids content of 20 %. The voids must be connected to the atmosphere. Figure 14 shows a typical graph of sound absorption coefficient versus frequency for a road surface with an excellent performance of sound absorption (porous asphalt concrete, denoted as PA see further Figure 14 Sound absorption coefficient vs. frequency. The closer the coefficient to 1, the higher the sound absorption capacity of the road surface in the concerned frequency range. In the example presented, sound absorption is high at frequencies of about 550; 1,800; 3,000 and 4000 Hz. The succession of absorption peaks and valleys is peculiar to a porous, granular material. The optimization process aims at adapting the first absorption peak in the low frequency range of the sound spectrum of the vehicle (around 500 Hz). More specifically, it is tried to obtain absorption peaks as high and wide as possible, so as to improve the absorptive properties of the road surface. The horizontal position, the height and the width of the first absorption peak is of particular relevance for the absorbing capacity of a porous road surface. The position is related to the thickness of the porous layer. The height and the width of the peak are related to the void content and the shape of the voids Synthesis Table 1 presents a synthesis of the various mechanisms involved in the production and propagation of tyre/road contact noise [Sandberg 2002]. The aggregate effect of these mechanisms is referred to as tyre/road contact noise. The representation made of, and weight given to, each of the mechanisms vary from one author to another. The SILVIA research programme has resulted in proposing a simplified model: It is reckoned that for standard rolling conditions tyre/road noise is mainly composed of impacts and shocks noise and air pumping noise, with the first mainly occurring below 1,000 Hz and the second mainly occurring above 1,000 Hz. Date: 30/04/2014 Version: (122)

30 Table 1 Various mechanisms involved in the production and propagation of tyre/road contact noise Effect Frequency Significance Impulses given to the tyres by contact between the texture of the road surface and the elements in the tyre tread, leading to tyre vibrations. Depends on speed, but always lower than 1,250 Hz +++ Mechanical impulses Stick-slip: shocks in the tyre tread elements. Audible in extreme cases as squealing in bends. High: > 1,250 Hz + Stick-snap: adhesion between the tyre tread and the road. High: > 1,250 Hz + Aerodyna mic impulses Sound amplificati on Air pumping: compression followed by sudden release of air trapped between the tyres and non-interconnected voids in the road. Horn effect: successive reflections of a sound wave in the conical area formed by the tyre and the road, which results in amplifying the sound. High: > 1,250 Hz khz +? Sound absorption (acoustic) Interconnected voids in the surface course absorb not only tyre/road contact noise, but also engine noise. This effect is more marked as the road surface is more permeable. Absorption depends on layer thickness, void content and shape Modelling of tyre/road noise Introduction: why modelling? Since the discovery of the main mechanisms of the tyre/road noise and the influence of the texture it has been a very attractive idea to model the tyre/road noise and hence to predict the acoustic quality of a pavement from its proxy-parameters : texture and in the case of a porous pavement from additional parameters describing the absorption effect. An adequate model would at least for impervious road surfaces make it possible to assess its acoustic properties from cheap, reliable and high speed texture measurements. This would be interesting for conformity of production checking of newly laid pavements and the monitoring of pavements in use. An adequate model would make it possible to undertake desk top design of new low noise pavements. Numerous models have been developed in the last three decades and they all can be classified according to three types of approaches: statistical models, analytical models and hybrid models. Statistical models use the correlation between the noise level(s) (e.g. one third octave band levels of the sound spectrum) emitted by a large number of vehicles and certain texture parameters (e.g. LMe, LMa, Ltx,80 mm, Ltx, Date: 30/04/2014 Version: (122)

31 5 mm, ). Analytical (also called physical ) models use a more or less detailed model of the tyre and calculate the emission of sound by that tyre when it rolls over a pavement with a given 3D texture. This can in principle be done in a precise way, but is generally a quite cumbersome calculation. The third category of models tries to combine the advantages of the two other categories and is a mix of the two. A good overview of the models already existing in 2001 can be found in [Kuijpers 2001]. A more recent survey can be found in [Sandberg 2010] Statistical models The Sandberg-Descornet model Sandberg and Descornet measured the pass by levels with four different car tyres on 33 different road surfaces and one looked at the correlation coefficient between the respective noise emission spectra and the texture spectra [Sandberg 1980]. Two areas of strong correlation were found (). The first area shows a strong positive correlation between the (mainly) megatexture range (maximum correlation at 80 mmm), and the low noise frequencies (< 1000 Hz). The second area exhibits a significant negative correlation between the short wavelengths in the macrotexture area (typically 5 mm) and the high noise frequencies ( Hz). The first area of correlation can be attributed to tyre vibrations induced by megatexture, the latter to the suppression of air pumping by the presence of fine macrotexture. This correlation has been confirmed by Clapp [Clapp 1985] and later by Noda e.a. [Nado 1998]. Using these strong correlations, one can make an assumption about the acoustic properties of a pavement using its texture spectrum. Descornet proposed the Estimated Road Noisiness Level (ERNL) for that purpose [Descornet 2001]. ERNL = L TX, 80mm 0.13 L TX, 5mm with L TX, 80mm and L TX, 5mm the texture levels in the octave band (expressed in db re. 1 µm) with centre wavelengths 80 mm and 5 mm respectively. ERNL is expressed in db(a) and is a figure approximating the SPB level, but it has no meaning as an absolute level: it can only be used to compare the acoustic properties of different pavements. It does not take into account absorption hence it can only be used for impervious road surfaces. Neither does it take into account the influence of the skew of the texture profile. It must be clear that ERNL is only a quite rough measure and the prediction accuracy is limited, making this value unsuitable for most applications (such as conformity of production checking or monitoring). Date: 30/04/2014 Version: (122)

32 Figure 15 Correlation between the noise emission spectrum and the texture spectrum as found by Sandberg and Descornet [Sandberg 1980] The TINO model The Italian TINO model [Domenichini 1999] is another statistical model, calculating the pas s by sound pressure level from a texture index which is derived from the texture spectrum. The importance of the model is rather limited as it is based on measurements on only seven dense road surfaces with one tyre. The model is based on the correlation of the texture index found with the sound pressure level Analytical models The Chalmers or Kropp model This analytical tyre/road interaction model developed by Chalmers in Sweden since the second halve of the 1990s includes both sound generation mechanisms and sound radiation properties of rolling tyres. The model is based on a very advanced tyre model, a fully non-linear contact model, and a radiation model including the surface of the road. The Kropp model is still developing. [Kropp 2013] The TRIAS model The TRIAS-model (Tyre-Road Interaction Acoustic Simulation) was developed at the end of the 1990s by TNO in the Netherlands. It was based on the early version of the Kropp model, but extended to excitation of the whole tyre-road contact area. Therefore in principal a 3D texture grid (x,y,height) is needed, but the model offers the possibility to create an artificial 3D texture grid from a 2D profile with a specially designed algorithm [Doelman 2004]. It includes calculation of the tyre vibrations and the air pumping phenomenon and it has a sound transmission module to calculate the noise level at the receiver position along the road. Date: 30/04/2014 Version: (122)

33 The TRIAS model has been validated using three sets of tyres (indicated with A, B and C) on DAC, ISO and PA pavements and the calculated pressure levels are plotted as a function of the measured values in Figure 16. If one calculates the standard deviation around the linear regression line one finds a value of 3.7 db(a). Figure 16 Sound pressure levels predicted with the TRIAS model versus measured values for three sets of tyres on several road surfaces. The solid line indicates a 1 to 1 relation and the dashed line ist he linear regression line The Bremner/Huff/Bolton model The Bremner/Huff/Bolton model [Bremner 1997] only deals with tyre vibrations and sound radiation, hence not with aerodynamic effects and absorption. The approach used is the Statistical Energy Analysis and the tyre is considered as an infinite cylindrical cell radiating sound as a flat plate. The spectrum at a distance of 7.5 m is calculated Hybrid models A general approach in the hybrid models is that they are based on calculated contact pressures between the tyre and the road surface The SPERoN model and AOT The SPERoN model [van Blokland 2007], see also homepage 7, has been developed over the past decade by a consortium consisting of M+P, Müller-BBM and Chalmers University. SPERoN is an acronym for Statistical Physical Explanation of Rolling Noise. 7 Date: 30/04/2014 Version: (122)

34 SPERoN is a hybrid model: i.e. the calculation of the tyre/road contact forces is done analytically, while the noise spectrum is calculated from tyre vibrations, airflow-related mechanisms, tyre friction, tyre cavity noise and aero-dynamic vehicle noise by means of a multivariate regression. The model was originally based on results from a measurement campaign on a series of test tracks constructed on an abandoned air field in Sperenberg near Berlin. It has later been extended in the FP6 project SILENCE and the Dutch noise innovation program (IPG). In the frame of IPG an extensive measurement campaign has been conducted on 42 different pavements on an abandoned highway in Kloosterzande in the south of the Netherlands. The model is able to predict the noise spectrum close to the tyre (CPX position) and at the side of the road (SPB position). The SPERoN - Acoustic Optimization Tool (AOT) is a commercial software application developed in 2009 and based on the SPERoN model that provides a graphical user interface for the interaction with the computational model which runs on a dedicated server on the internet, especially designed for the development of low noise pavements. The AOT offers some additional features for this purpose. The user can investigate the effect of changes of the road surface such as changing the texture (i.e. roughness), the acoustic absorption and flow resistance (related to porosity) and the dynamic stiffness. In a Danish study [Oddershede 2012] one compared on site measured SPB and CPX levels with the values predicted with the AOT. The SPB values were systematically underestimated by AOT with about 3 db(a) and the CPX levels were underestimated between 0,3 and 2,5 db(a). The AOT failed to predict correctly the SPB spectra below 1 khz (see Figure 17). The predicted CPX spectra showed important and less systematic deviations from the measured spectra (see Figure 18). Figure 17 Comparison of a measured SPB sound spectrum (solid blue line) with the AOT prediction (gray dashed line) for a low noise asphalt concrete [Oddershede 2012] Date: 30/04/2014 Version: (122)

35 Figure 18 Comparison of a measured CPX sound spectrum (solid blue line) with the AOT prediction (gray dashed line) for a low noise asphalt concrete [Oddershede 2012] Earlier, in a conference in 2009, a limited validation of SPERoN was presented [Auerbach, 2009]. The outcome is presented in Figure 19. The result is also quite discouraging: one of the 8 surfaces is predicted to be 5 db noisier than when measured, two others differ by ±2.5 db from the measured values. Figure 19 Validation of SPERoN model for eight surfaces made in the French-German project DEUFRAKO [Auerbach, 2009]. Date: 30/04/2014 Version: (122)

36 The German-French HyRoNE model The DEUFRAKO project 8 developed a model itself, called HyRoNE. Information about this model can be found in [Klein, 2009]. The corresponding results for that validation are shown in Figure 20. The results of this model are hardly better than for SPERoN, as the maximum difference between model and measurement is 5 db and there is a spread of up to ± 2 db around the regression line. Figure 20 Validation of HyRoNE model for a large number of surfaces made in the French- German project DEUFRAKO [Auerbach 2009]. 8 A German-French scientific collaboration, founded in 1978, for more information, see Date: 30/04/2014 Version: (122)

37 3 The influence of macro- and megatexture on rolling resistance 3.1 Background Rolling resistance is an important characteristic which is worldwide acknowledged for tyres. Road surfaces however play also a significant role in the rolling resistance story. This is shown by a lot of research which already started in the 1980 s and which is still going on. Rolling resistance is an interaction between tyres and road surfaces and therefore road surfaces should not be neglected. Until now rolling resistance has not been a determining factor for the selection of pavements. Other characteristics like skid resistance and cost are always considered. It would be interesting to include this characteristic in the selection phase. When a pavement with low rolling resistance is chosen, all road users consume less energy and produce less CO 2 emission, which is good for the environment. When only the option of using low rolling resistance tyres is considered, it is up to the individual road users to decide whether they want to invest in it or not. Not much importance is given to the rolling resistance of road surfaces as there is a lack of practical measurement methods and thereby lack of measurements. In order to be able to measure rolling resistance of pavements correctly and to be able to compare performed measurements, harmonisation and normalisation of measurement methods is necessary. As measurements of rolling resistance on the road are complex one could think of a model based on road parameters which can be measured more easily, like texture, unevenness, Therefore first a good understanding of all these influencing parameters is needed. A lot of work has already been done in the frame of the MIRIAM project: Large parts of this overview are based on reports that were produced for this project, namely [Bergiers, 2011], [Sandberg, 2011], [Sandberg et al., 2011] and [Sandberg, 2012]. In this chapter an overview is given of research performed regarding the influence of texture and unevenness on rolling resistance. 3.2 Definitions According to [Sandberg, 2012], the most relevant measure of rolling resistance, as well as practical to use, seems to be something based on the force (Frr) that is required to move the rolling tyre in the desired direction. However, it is clear that this force will depend on the load that is applied to the wheel and thus the tyre. Studies have found an approximately linear relation between rolling resistance and wheel load, represented by a force Fz (equal to the mass m of the load times the gravitational constant g). Since wheel loads can vary under different conditions, a near-constant coefficient, rolling resistance coefficient, RRC or Cr (both terms are commonly used), has been created to represent the characteristic of tyre/road rolling resistance: the dimensionless ratio of rolling resistance to wheel load: RRC = Cr = Frr/Fz. ISO uses the notion Cr. Thus: Rolling resistance coefficient (RRC) = Cr = Frr/Fz Date: 30/04/2014 Version: (122)

38 where the forces Frr and Fz (see the preceding paragraphs) are magnitudes and not vectors. This coefficient in turn depends on several tyre and road surface parameters as well as (to some extent) the vehicle speed. It is important to note that the RRC or Cr is a relative measure. It can be used to compare tyres and pavements. 3.3 Texture levels Various researches have investigated the relation between RRC and texture levels. Rolling resistance measurements were performed with fuel consumption, trailer and coast down method. In general one can state that a higher correlation was found for the megatexture range than for the macrotexture range Measurements in Sweden In 1983, Sandberg at VTI and his colleagues made measurements of fuel consumption of a Volvo 240 car on 20 road surfaces, cruising at 50, 60 and 70 km/h, with a variation in surface type and texture that covered most of the Swedish range at that time. He considered the fuel consumption (FC) differences as approximately ¼ of corresponding rolling resistance differences when transforming results to rolling resistance. Texture and "shortwave unevenness" (wavelengths m) were measured by means of a mobile laser profilometer mounted in an exceptionally soft-suspended luxury car. The tyres were Pirelli Cinturato C3 175SR14. Results were published no earlier than in 1990 [Sandberg, 1990]. The best correlation of FC was obtained with the shortwave unevenness (R = 0.91). Megatexture level came second (R = 0.83) and macrotexture third (R = 0.60). This was illustrated when correlation between FC and road roughness/texture level as a function of texture wavelength was calculated; see Figure 21. Date: 30/04/2014 Version: (122)

39 Unevenness Megatexture Macrotexture Figure 21: Correlation between fuel consumption per km and road roughness/texture level as a function of texture wavelength [Sandberg, 1990]. When making fuel consumption measurements, in contrast to trailer measurements, the suspension losses are clearly present and they should peak in the area where Sandberg's data peak. It was assumed that the suspension system of the test car was in excellent condition, but it was not tested Measurements in Belgium Research Descornet Using a special rolling resistance trailer, as well as a profilometer for the texture range and another one for the unevenness range, Descornet at BRRC analysed the relation between RRC and unevenness, megatexture and macrotexture [Descornet, 1990]. The test tyre was a pattern-less Michelin SB 14" tyre. Date: 30/04/2014 Version: (122)

40 Figure 22: Correlation between RRC and road roughness/texture level as a function of texture wavelength [Descornet, 1990]. The dashed line is the result when the six (concrete) pavements that had transverse textures (out of 37 pavements) were neglected. Descornet found that the most sensitive spectral range was the megatexture range [Descornet, 1990]. See Figure 22. It appeared that the most sensitive range is megatexture, but that macrotexture is also very influential, at least when disregarding the six sections that were transversely grooved. In French research (see 3.5.1) from calculations of energy loss in shock absorbers (dampers) as a function of roughness level, it was shown that the wavelength range 1 m < λ < 3.3 m, was by far the most important one for suspension losses. In this range also a lower correlation can be seen in Figure 22. The diagrams of Figure 21 and Figure 22 look quite different. However, in a way, the difference is logical, since when making fuel consumption measurements, in contrast to trailer measurements, the suspension losses are present and they should peak in the area where Sandberg's data peak. It may be noticed that in the megatexture and macrotexture areas Descornet's and Sandberg's data are not very different Research Artesis In there had been a cooperation project between BRRC and the Artesis University College of Antwerp, in which students from Artesis had access to the BRRC trailer for making rolling Date: 30/04/2014 Version: (122)

41 resistance measurements. Earlier, other students had also made coastdown measurements of rolling resistance, using texture equipment and test vehicles from BRRC. In one part of the Artesis-BRRC project the RRC measurements were compared with texture spectral level measurements. From the results of the measurements the correlations (R²) between the RRC and the texture levels of the test sections were calculated; per each octave band in the texture spectra. All results are shown in Figure 23. The blue graphs represent results by Aerts-Cools [Aerts and Cools, 2010] for coast down measurements with vehicle A and B. The green line is the result of research performed with BRRC trailer by Dotsenko-Helsen [Dotsenko and Helsen, 2010], Artesis students in The red line displays results obtained by Descornet in the eighties with the old BRRC trailer [Descornet, 1990]. Macrotexture Megatexture Figure 23: Comparison of RRC - texture spectra correlations obtained in the Artesis-BRRC project with previous research. The results of Descornet and the coast down method from Aerts-Cools give better correlations. In contrast with previous research, very low correlations are found in the latest research with the BRRC trailer by students De Bie Hofmans [De Bie & Hofmans, 2011]. An explanation for this is probably that the trailer as it was used for this research still had a lot of uncertainties (change of tyre, new sensors, change of tyre temperature measurement, resulting in calibration issues). Because of the many uncertainties, the curve is not presented in this figure. More results of the Artesis project are reported in [Bergiers, 2012]. A typical contact area length of a car tyre is 0.15 m, depending on other parameters like tyre inflation pressure, load, type The maxima of the graphs Dotsenko-Helsen and Aerts-Cools (B) are situated around 0.16 m wavelength, which is about the same dimension as the contact area length. One can state that this contact area length probably has a large influence on rolling resistance. Date: 30/04/2014 Version: (122)

42 3.3.3 Pilot study MIRIAM In the frame of the MIRIAM project a round robin test with various rolling resistance measuring equipment was organized in June 2011 [Bergiers, 2011]. It was the first time that measurements were compared between three different rolling resistance trailers (BASt, BRRC and TUG). Various tyres were used, among them also two reference tyres SRTT and AAV4. Measurements were performed at two different speeds. The correlation between rolling resistance and one-third-octave band texture levels (measured by BRRC) has been determined based on rolling resistance measurements performed by TUG on different test sections. The correlation has been calculated for texture levels without and with applying the enveloping procedure to the texture [von Meier, 1992] for two speeds: 50 and 80 km/h. Figure 24 shows the correlation between the RRC values for the tyres tested at 50 (full line) and 80 km/h (dashed line) and texture spectral level. This is shown as R² for each one-third-octave texture spectral band as a function of its texture wavelength. Each color represents a tyre tested by TUG. Macrotexture Megatexture Figure 24: Correlations at various texture wavelengths and tyres for measurements performed at 50 and 80 km/h without applying the enveloping procedure to the texture. Figure 25 shows the same as the previous one but with applying the enveloping procedure to the texture. Please do not pay any attention to the range at shorter wavelengths than 0.02 m since the strange shape of the curve with a peak and a dip there is entirely due to an artefact of the enveloping procedure. Only the part with wavelengths longer than approximately 0.02 m should be considered. Date: 30/04/2014 Version: (122)

43 Invalid range Macrotexture Megatexture Figure 25: Correlations at various texture wavelengths and tyres for measurements performed at 50 and 80 km/h with applying the enveloping procedure to the texture. Michelin Energy Saver tyres (ES16 and ES14) show the highest correlations for most texture wavelengths. The highest correlations for all tyres are found at the longest texture wavelengths. The results for SRTT and AAV4 lie very close together. The correlations with enveloping are better than without enveloping. More results of this study in relation to other texture parameters are reported in 3.4.2, and Measurements in Minnesota The Minnesota Road Research Project (MnROAD) was constructed between 1990 and 1994 by the Minnesota Department of Transportation (MnDOT). It consists of a wide range of different pavements. Each test section is approximately 150 m long [Wilde, 2012]. Rolling resistance measurements were conducted using the TUG trailer in September 2011 on 54 different test sections, including asphalt and portland cement concrete (PCC) pavements, some of them with very special textures. Pavement surface texture measurements were performed by MnDOT using a mobile, line-laserbased, texture profiler in longitudinal and transversal directions. An excellent database was collected by MnDOT, including data about MPD, skewness, rms, third-octave band texture levels, but also IRI, skid resistance and noise levels measured by the OBSI method. Linear regression analyses were performed using the database in order to search for a dependency of RRC on other surface characteristics. Up to four dependent variables were used for the regression analyses (texture, friction, roughness and road). Results were compared and reported in [Sohaney, 2013]. Date: 30/04/2014 Version: (122)

44 For the regression analyses pavements were grouped to improve the correlations. The following groups were formed: asphalt pavements, non-grind PCC pavements including transversally and longitudinally textured PCC and grind PCC pavements including conventional and next generation diamond grinds. Additionally Mainline and Low-Volume Roads were treated separately as a qualitative variable to yield better results implying there is an undetermined important factor missing in the data. The equations that were selected based on prioritising simpler and common concepts are shown hereunder. Note that other equations with different parameters, which are not mentioned here, yielded good correlations too. For the asphalt grouping the following equation reached a R² value of 0.80: For non-grind PCC pavements a R² value of 0.77 was obtained by using three variables: For grind PCC pavements a R² value of 0.93 was reached by using the following equation: Where Texture, L 3.15 to 50 mm and Texture, L 50 to 160 mm are broad band texture levels calculated by energy summing specific third-octave waveband levels as defined by ISO Roughness, L 1.25 m and Roughness, L 2.0 m are roughness levels in third-octave wavelength bands. 50%TrSKEW is the 50 th percentile value of transversal skewness. Road is a qualitative variable which treats Mainline (value one) and Low-Volume Roads (value zero) separately. Regression analyses with a one-third-octave band texture level as a single variable yielded good correlations for the asphalt grouping. Correlations from 0.77 to 0.81 were obtained for one-thirdoctave band texture levels 3.15 mm and 20 to 80 mm which is a large part of the macrotexture and a small part of the megatexture range. Unfortunately not all results are reported in [Sohaney, 2013]. Only the top ten of best correlations are shown in the report. It might be that other parts of the megatexture range yielded fair correlations too, but that those were much lower than the correlations mentioned above. Date: 30/04/2014 Version: (122)

45 Results from the same research related to MPD, rms and skewness may be found in sections 3.5.8, and respectively Overview Rolling resistance measurements made using the fuel consumption, trailer and coast down methods show a good correlation with macro- and megatexture. In general the RRC shows even better correlations for the megatexture range than for the macrotexture range. 3.4 Macrotexture and megatexture levels Measurements in Sweden In the Swedish research that already was introduced in section 3.3.1, the correlation was studied between fuel consumption and macrotexture level in the wavelengths range mm with the results presented in Figure 26 [Sandberg, 1990]. Figure 26: Relation between fuel consumption (FC) at 60 km/h and macrotexture level L MA in the mm texture wavelength range. Diagram scanned from [Sandberg, 1990]. The correlation coefficient between FC values averaged for the three speeds 50, 60 and 70 km/h was 0.60 when correlating with macrotexture level LMa. A much better correlation was found with megatexture (see Figure 21) Pilot study MIRIAM An extensive measurement campaign for rolling resistance was set up in Nantes at the test track of IFSTTAR in the frame of the MIRIAM project (see also 3.3.3). Measurements by the trailer method Date: 30/04/2014 Version: (122)

46 were done by MIRIAM partners TUG, BASt and BRRC at different speeds (50 and 80 km/h) and with various tyres. More details can be found in [Bergiers, 2011]. The main conclusions drawn from this campaign with regard to LMa and LMe (see also [Sandberg et al., 2011]) are: A better correlation is found with LMe than with LMa (see Figure 27 and Figure 28). Applying the enveloping procedure is consistently successful: it increases the correlation. Note that both macro- and megatexture levels are calculated based on the rms value of the profile filtered in the macrotexture, respectively megatexture ranges. It means that they are not sensitive to the direction of the profile curve whether it is a positive or negative profile. However when applying the enveloping, this problem is solved as mainly the part of the profile in contact with the tyre is taken into account. More detailed results of this study with respect to MPD are reported in and Without enveloping R² 1 0,9 0,8 0,7 0,6 0,5 0,4 0,3 0,2 0,1 0 ES14/BRRC_50 ES14/BRRC_80 SRTT/BASt_50 SRTT_BASt_80 AAV4/BASt_50 AAV4/BASt_80 ES16/BASt_50 ES16/BASt_80 Trailer-Tyre-Speed combination SRTT/TUG_50 SRTT/TUG_80 AAV4/TUG_50 AAV4_TUG_80 ES16/TUG_50 ES16/TUG_80 ES14/TUG_50 ES14/TUG_80 Figure 27: Summarizing graph correlations MPD, LMa and LMe for all tyres and speeds without enveloping [Bergiers, 2011]. MPD LMa LMe Date: 30/04/2014 Version: (122)

47 With enveloping R² 1 0,9 0,8 0,7 0,6 0,5 0,4 0,3 0,2 0,1 0 ES14/BRRC_50 ES14/BRRC_80 ES16/BASt_80 SRTT/BASt_50 SRTT_BASt_80 AAV4/BASt_50 AAV4/BASt_80 ES16/BASt_50 Trailer-Tyre-Speed combination SRTT/TUG_50 SRTT/TUG_80 AAV4/TUG_50 AAV4_TUG_80 ES16/TUG_50 ES16/TUG_80 ES14/TUG_50 ES14/TUG_80 Figure 28: Summarizing graph correlations MPD, LMa and LMe for all tyres and speeds with enveloping [Bergiers, 2011]. To eliminate uncertainties only correlations equal or higher than 0.7 were considered to estimate an overall average slope coefficient. Based on Figure 29 one can state that the slope coefficients without enveloping are independent of speed or institute but possibly dependent of tyre type. LMa and LMe have an overall average slope coefficient of and respectively. The range of slope coefficients for LMa is and for LMe is MPD LMa LMe LMa and LMe slope coefficients: Average and standard deviations Without enveloping 0, ,0003 Slope coefficient 0, ,0002 0, ,0001 LMa LMe 0, overall BASt TUG AAV4 ES14 ES16 SRTT Figure 29: Average values and standard deviations LMa, LMe per category (overall, speed, institute, tyre) without enveloping [Bergiers, 2011]. Based on Figure 30 one can conclude that the slope coefficients with enveloping are independent of speed or institute but possibly dependent of tyre type. LMa and LMe have an overall average slope Date: 30/04/2014 Version: (122)

48 coefficient of and respectively. The range of slope coefficients for LMa is and for LMe is LMa and LMe slope coefficients: Average and standard deviations With enveloping 0,0003 0,00025 Slope coefficient 0,0002 0, ,0001 LMa LMe 0, overall BASt TUG AAV4 ES14 ES16 SRTT Figure 30: Average values and standard deviations LMa, LMe per category (overall, speed, institute, tyre) with enveloping [Bergiers, 2011]. The enveloping procedure yields higher slope coefficients for LMa and LMe Overview Best correlations are found with megatexture levels. Applying the enveloping procedures yields even better correlations. 3.5 Mean Profile Depth Measurements in France In France rolling resistance measurements were performed in the 1980s on a test track in Nantes. The results were reported in [Laganier & Lucas, 1990] and [Delanne, 1994]. It appeared that over the range of MTD from approx. 0 to 5 mm, RRC increased from to This would correspond to a slope in RRC versus macrotexture (MPD) of ; which was a bit lower than results from research presented in the next sections. French fuel consumption measurements showed an extra consumption of fuel of up to 6 % for a car with an average fuel consumption of 7 l / 100 km as influenced by unevenness and 5 % as influenced by macrotexture (MTD variation was mm). Date: 30/04/2014 Version: (122)

49 3.5.2 Measurements in Belgium In research performed in 1990 by Descornet and introduced in section [Descornet, 1990], the slope in the RRC vs texture depth diagram is (see Figure 31). This value is fairly consistent with more recent results. Figure 31: RRC versus texture depth [Descornet, 1990] Measurements in New Zealand [Cenek, 1994] reported on a rolling resistance range of 55 % between the best and the worst pavement, from tests in New Zealand ( ), with MTD values ("sand circle" equal to "sand patch") varying from 0.6 to 2.7 mm and unevenness varying between 37 and 59 NAASRA counts/km (corresponding to IRI of 1.4 to 2.3). The result according to [Cenek, 1994] was expressed as the following equation: RRC = x MTD x 10-3 x IRI If we assume that MTD = MPD (they are usually rather close) and neglect the nonlinearity of the equation above, this would correspond to a slope of in the equation of RRC versus MPD. In another example, if we assume that MTD is 1.0 mm, an IRI of 0.5 will give RRC = , while an IRI of 2.5 will give RRC = This is an increase in rolling resistance of 18 % for an IRI increase from 0.5 to 2.5. This may suggest that the influence over the range of IRI on a common paved road network would amount to approximately half that of the range of macrotexture. Again, this shows the importance of macrotexture and that unevenness is a parameter that shall not be neglected (see also 4.4). Date: 30/04/2014 Version: (122)

50 3.5.4 Measurements in Poland Results of tests made on the rolling resistance drum facility at TUG in Gdansk in a VTI-TUG project including measurements of RRC on approximately 100 car tyres, on two very different drum surfaces, one smooth sandpaper (estimated MPD of 0.12 mm) and one surface dressing with max. 11 mm chippings (estimated MPD of 2.4 mm), resulted in a slope of the RRC versus MPD as [Sandberg et al, 2011]. A very slight speed dependence of the MPD - RR relationship was found (measurements were actually made at the three speeds 80, 100 and 120 km/h). Another set of car tyres was measured a few years later in the EU project SILENCE [Sandberg et al, 2008]. The surfaces were the same, but the tyres were six tyres chosen to be representative of popular market tyres. They were tested in new condition and at various states of wear (8, 6, 4 and 2 mm tread depth); i.e. for 24 tyre/tread depth combinations. The result was a slope coefficient of as an average for all tread depths, and for full tread depth and for 2 mm tread depth. It suggests that worn-out tyres are a little less sensitive to texture than new ones Measurements in Sweden and Denmark In a measurement campaign with the TUG trailer on behalf of VTI, Sweden, funded by the NordTex project, a number of road surfaces in Sweden and Denmark were measured in the past five years using the TUG R² trailer [Sandberg, 2011]. A compilation of such RRC data, plotted as a function of MPD, appears in Figure 32. Note that the two points in the upper right corner are for a chip seal (in two different tracks) which had some potholes and were measured at temperatures near freezing point. Date: 30/04/2014 Version: (122)

51 0,015 0,014 y = 0,0017x + 0,0121 RRC for average of the three test tires 0,013 0,012 0,011 0,010 0,009 y = 0,002x + 0,0098 R 2 = 0,9466 y = 0,0022x + 0,0087 R 2 = 0,8224 y = 0,0021x + 0,0079 R 2 = 0,976 y = 0,0016x + 0,0079 R 2 = 0,7712 y = 0,0017x + 0,0087 R 2 = 0,7604 0,008 0,007 0,0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1,0 1,1 1,2 1,3 1,4 1,5 1,6 1,7 1,8 1,9 2,0 MPD [mm] Figure 32: Results of Swedish-Polish studies of the relation between RRC and macrotexture, for car tyres, where macrotexture is represented by the MPD. A certain cluster of points and associated regression line belong to the same data set, measured within a few days, whereas the data sets have been collected in The data in grey and black are from measurements in Other colors are from 2009 [Sandberg et al., 2011]. Unless the regression lines do not coincide, according to [Sandberg, 2011], probably due to lack of temperature correction (although with temperature corrections regression lines did not coincide perfectly either), and/or some uncontrolled calibration error giving a certain bias in some data sets, it is apparent that the slopes of the lines are consistent. The slope indicates the sensitivity of RRC to a change in MPD. The regression line constants which are plotted in the diagram show that the slope varies between and [Sandberg, 2011]. The LMa values from earlier reserach in Sweden, reported in section 3.4.1, can be transformed to MPD to yield: RRC = constant x MPD. This is in line with the result reported in the Swedish- Polish studies. It is interesting to study whether porous road surfaces (single- or double-layer porous asphalt) show different rolling resistance properties than dense surfaces do, assuming that the macrotexture is measured as MPD in both cases. For this reason, a few porous surfaces have been included in rolling resistance measurement series. First, Figure 33 shows measurements made in Denmark in 2009, where one of the surfaces was porous. Obviously, it seems that the MPD overestimates the RRC of this surface. Date: 30/04/2014 Version: (122)

52 0,0140 0,0130 0,0120 y = 0,0017x + 0,0087 R 2 = 0,7604 RRC for average tyre 0,0110 0,0100 0,0090 0,0080 0,0070 0,0060 0,40 0,60 0,80 1,00 1,20 1,40 1,60 1,80 2,00 MPD [mm] Figure 33: RRC versus MPD for a measurement series in Denmark, where the red symbol shows the result for a porous asphalt with maximum 8 mm chippings [Sandberg et al., 2011]. From coast down measurements by VTI, during the years , the relationship CRR and some important variables was deducted for car and truck tyres. The results of the car tyres are shown in Table 2. Table 2: Best estimations of coefficients for the basic rolling resistance (Cr) and the most important variables in the model equation, describing the contributions of the parameters to the rolling resistance coefficient. Data from [Karlsson et al., 2011]. Parameter (term in equation) Coefficient (slope) - Car Cr00 (basic rolling res. constant) CrMPD (macrotexture influence) CrIRI (unevenness influence) CrTemp (temperature influence) * To be replaced by more recently obtained coefficients The standard deviation for CrMPD was Results show that the effect of unevenness is in general significantly smaller than that of macrotexture; albeit not negligible Pilot study MIRIAM Main conclusions drawn from the MIRIAM pilot study (see [Bergiers, 2011] and [Sandberg et al., 2011]) with regard to MPD are (see Figure 27 and Figure 28): A good correlation (0.8 to 0.9) between MPD and RRC at different vehicle speeds (50 and 80 km/h) is found by trailer measurements performed by BASt and TUG. Measurements with the BASt trailer (for the AAV4 tyre at 80 km/h) and with the BRRC trailer have given unsatisfactory results, possible explanations are mentioned in [Bergiers, 2011]. Date: 30/04/2014 Version: (122)

53 Enveloping the texture does increase correlations significantly (up to 98 %). Correlations between MPD and RRC are better than LMe and LMa. Rolling resistance at 80 km/h gives (slightly) higher correlation with texture than at 50 km/h. To eliminate uncertainties only correlations equal or higher than 0.7 were considered to estimate an overall average slope coefficient. Based on Figure 34 one can state that the slope coefficients without enveloping are independent of speed or institute but possibly dependent of tyre type. MPD has an overall average slope coefficient of The range of slope coefficients was The slope coefficients without enveloping are independent of speed or institute but possibly dependent of tyre type. MPD has an overall average slope coefficient of , which is higher than without enveloping. This higher slope coefficient with enveloping was also noticed for LMa and LMe (see 4.4.2). The range of slope coefficients was Figure 34: Average values and standard deviations MPD per category (overall, speed, institute, tyre) with and without enveloping German study truck trailer In the frame of a German research project about the rolling resistance of truck tyres [Bode, 2013], in 2011 measurements have been performed on the same test sections in Nantes as for the MIRIAM pilot study (see 3.5.6), using a truck towing a large trailer with truck tyres. Tyres Michelin XTA2+ Energy with dimension 385/65 R L were used on the trailer. Measurements were performed at 15 km/h in both directions. RRC for trucks was found to be much smaller than RRC for cars. Date: 30/04/2014 Version: (122)

54 An excellent correlation was found with MPD (R² = 0.90) yielding the following equation: RRC = 0.74 x MPD The results are shown in Figure 35. Figure 35: Correlation between MPD [mm] and RRC [ ] [Bode, 2013] Measurements in Minnesota Based on measurements performed in Minnesota, research introduced in section 3.3.4, the relation between CRR and MPD was studied. The results were very surprising and inconsistent compared to the European experience. Only a relatively low correlation between CRR and MPD was found with slopes of to The authors name following possible causes [Wilde, 2012]: - Variation of temperature during measurements and absence of suitable temperature correction procedure for trailer measurements - Problems with texture measurements; many special textures in dataset In further analyses transversal and longitudinal MPD were studied separately as clear differences were seen for the PCC pavement grouping [Sohaney, 2013]. MPD range was from 0.3 to 2.3 mm. Linear analysis was performed with MPD and IRI as variables but no good results were obtained. Texture friction analyses were performed with 105 texture and 4 friction variables giving 420 possible combinations for each pavement grouping. Many combinations yielded valid regression results. In the asphalt grouping 90 th percentile longitudinal and transversal MPD and 50 th percentile transversal MPD yielded correlations of 0.75 in combination with HFT 9. 9 Friction number measured by a Dynatest 6875H Highway Friction Tester (HFT), a continuous friction measuring device. Date: 30/04/2014 Version: (122)

55 Texture texture analyses were performed with 588 combinations of texture variables. 50 th percentile transversal and longitudinal MPD yielded 0.76 R² in combination with the 5 mm texture waveband variable for the asphalt grouping Measurements in the Netherlands In April 2013 an extensive measurement program has been conducted in the Netherlands to investigate the influence of different road surface types on rolling resistance [Hooghwerff, 2013]. Texture and rolling resistance measurements have been performed on 69 road sections. On 32 additional road sections only rolling resistance has been measured. Rolling resistance measurements have been performed with the trailer of TUG. The data set contains smooth to rough textures and mainly includes porous asphalt concrete, dual layer porous asphalt concrete, dense asphalt concrete, thin surface layers and some others like SMA and a surface dressing. New surfaces were one to three years old, while old surfaces were 9 to 12 years old. A temperature and tyre pressure correction has been applied based on measurements that were repeated on a reference surface. The relation between some typical texture parameters and rolling resistance has been investigated. MPD showed a good correlation with the RRC, which is expressed in kg/t (RRC*). The regression analysis yielded following equation (R² = 0.68): The 95 % confidence interval is also given. Less dependance was found than in the MIRIAM project (see 3.5.6, slope coefficient of compared to ). This may be explained by the negative skewness of the Dutch pavements. Almost all surfaces, except three, have a negative skewness (see Figure 46). It is more in line with measurements performed in Minnesota (see 3.5.8). A slightly better fit (R² = 0.71) was found for the following multiple regression model including rms: The model uncertainty has been determined to be 3 %. A more detailed view is given in Figure 36. Date: 30/04/2014 Version: (122)

56 Figure 36: Rolling resistance coefficient (expressed in kg/t) versus MPD for all road sections. Different colours represent various road surface types. Open circles indicate road sections older than 8 years [Hooghwerff, 2013] Overview In Europe the MPD has so far consistently appeared to be a measure with very good correlation with rolling resistance. If one would try to estimate a summary for all experiments reported here, except for the research in France, Minnesota and the Netherlands, the slope coefficient should be in the range In France a smaller slope coefficient of was found, in Minnesota to and in the Netherlands , probably due to the negative skewness of test sections. Only one research project investigated the relation between MPD and truck RRC yielding a slope coefficient of Date: 30/04/2014 Version: (122)

57 3.6 Root Mean Square (rms) macro- and megatexture Measurements in Germany Trailer measurements An early version of the BASt trailer for rolling resistance was used in the 1990s to make measurements on 10 surfaces on the German motorway A555 [Ullrich et al, 1996]. These included the use of four different (car) test tyres. Results were presented as "normalized RRC at 25 C". Probably, "normalized" just referred to temperature correction according to ISO to a reference temperature of 25 C. Measurement of the textures of the same surfaces was made by means of a laser profilometer. The results were presented as rms values of the profile curves, filtered in three different texture wavelength ranges: mm "fine texture" mm "coarse texture" mm "megatexture" However, the texture rms values were normalized to proportions relative to one surface and that surface was given the value 1.0 mm. Therefore, all texture values are just relative to this surface. They cannot be compared to any "modern" standardized measures. Figure 37 shows the result for the case of texture in the mm texture wavelength range, which was the range that gave the best correlation between C r and texture. Diagrams for the other two texture ranges show similar results (correlation coeff for "fine texture" and 0.67 for "megatexture"). Despite the higher correlation for "coarse texture", 0.75 versus 0.67 for "megatexture", according to Ulf Sandberg [Sandberg et al, 2011], one should not conclude that megatexture is less important than "coarse texture" (macrotexture) since the poorer correlation is entirely due to the two smooth surfaces not being so extremely smooth in the megatexture range as they are in the two macrotexture ranges. Date: 30/04/2014 Version: (122)

58 Figure 37: Relation between RRC (probably average of four car tyres) and rms value of "coarse texture" for the 10 tested road sections. Texture values are given as a proportion of the texture of one of the surfaces (the rightmost data point, which is set as 1.0 mm). [Sander, 1996]. These German measurements also showed that the four car tyres had approximately equal correlations to the rms texture in the three texture ranges; see Figure 38. Figure 38: Relation between RRC for the four car tyres A10-A13 and rms value of the three texture ranges for the 10 tested road sections. FT = fine texture, GT = coarse texture and MT = megatexture. [Sander, 1996] Drum measurements BASt used their drum facility PFF (PFF = Prüfstand Fahrzeug Fahrbahn, see description in [Sandberg (ed); 2011]), to make measurements on 11 surfaces mounted successively on the drum [Ullrich et al, 1996][Sander, 1996]. Of the surfaces which were examined, three were produced as close replicas of Date: 30/04/2014 Version: (122)

59 real road surfaces and two surfaces were constructed as ISO surfaces ; however, becoming much too smooth according to this author. The remaining ones were sandpaper-like surfaces with various grit sizes. The rolling resistance measurements included the use of four different (car) tyres. Results were presented as "normalized RRC at 25 C". Texture measurement results of the laser profilometer were presented in three texture ranges (same principle as specified in ). Figure 39 shows the average RRC for the tyres, distinguishing between the four dimensions used, over 10 of the 11 drum surfaces. In the figure the three rightmost surfaces were asphalt concrete with gradations indicated and the four in the middle were asphalt surfaces with chippings spread on the surface of various indicated gradations. The two rougher of these should be possible to use on real roads. The three surfaces at the left were various sandpaper-type surfaces. The four tyres ranked the surfaces in a very similar way; there is just a certain bias between the four curves /65 R15 H 205/60 R15 V 175/70 R13 T 155/70 R13 T Mittl. Rollwiderstand R 25 / [N] Korn/ mm: 30 0,6-1,0 P24 0,7-1,2 P20 1,0-1,7 P16 1,0-1,7 Kunstharz 0,7-1,4 2,0-2,8 4,0-5,6 8,0-11,0 0/8 ISO unbeh. 0/8 ISO beh. Art/ Träger: Korund/ Schmirgelleinen Splitt/ Kunstharz Asphaltbeton Figure 39: Average RRC values from drum tests - two runs for each of four tyres at three different speeds (50/90/120 km/h) on 10 of the 11 drum surfaces [Sander, 1996]. Figure 40 shows the relation between the average RRC values and the macrotexture, the latter expressed as rms value of the profile within the texture wavelength band mm. Similar diagrams were reported also for the other two texture ranges, but as they gave lower correlations they are not reproduced here. It is interesting to note that the highest correlations here were obtained for the fine macrotexture. This is opposite to all other studies. Probably, it has to do with the selection of the surfaces in the test program, since 7 of the 10 surfaces were too smooth or had too small chippings to be realistic of real roads. Nevertheless, it is notable that the relation appears to be rather linear even down to the very smooth textures in this test program. 0/11 S beh. Date: 30/04/2014 Version: (122)

60 Figure 40: Relation of rolling resistance to fine macrotexture ( mm) for drum tests (using tyre 175/70 R13T) [Sander, 1996] Compilation of trailer and drum measurements A compilation of the correlation coefficients between rolling resistance and texture in the three bands, for both the trailer (see ) and the drum measurements (see ) is shown in Table 3. Table 3: The correlation coefficients between rolling resistance and texture in the three bands, for both the drum and the trailer measurements. Texture parameter Drum Trailer Fine texture mm Coarse texture mm Megatexture mm A compilation of the rolling resistance coefficients measured on four tyres, both on the drum and on the road (a very smooth surface) is shown in Table 4. Date: 30/04/2014 Version: (122)

61 Table 4: Average rolling resistance coefficient values (temperature corrected) in %, measured on the drum and compared with trailer measurements on a road surface. Tyre type Drum (PFF) Trailer (A555/H) A A A A Measurements in UK As part of the so-called MARS project, in the 1990s Parry at TRL conducted a study of relations between functional properties of road surfaces and texture [Parry, 1998]. This included some rolling resistance measurements made on the indoor drum facility at the Dynamics Laboratory at Dunlop Tyres Ltd in Birmingham. The drum was equipped with various epoxy replicas of real road surfaces, reproducing the texture of these; apart from the original smooth steel and sandpaper. In addition, four special surfaces with simple geometric asperities were cast from polymer resin and mounted on the drum. They were made with special geometrical patterns; see Figure 41. It is obvious that these surfaces had very special profile asymmetries. Figure 41: The special geometrically patterned surfaces produced for the tests. From left to right: hemispheres cubes tetrahedral discs [Parry, 1998]. For the rolling resistance measurements, three tyre types were used: a smooth-patterned (PIARC), a standard and a wide car tyre. The texture measurements are interesting. Parry measured the rms value of the texture profile. Then he divided the rms value into two parts: The "contact" part (rms c ) which is the rms value of the part of the profile which is in contact with the tyres The "non-contact" part (rms v ) which is the rms value of the part of the profile which is not in contact with the tyres (subscript v is for "voids") The total rms value (= the arithmetic sum of rms v and rms c ) is denoted rms t. In principle, Parry in this way by rms c designed an enveloping function. He made the division of rms into two parts by means of special software which had been developed to predict tyre/road contact areas. It was based on the finding that by mathematically analysing the profile measured by laser, the contact area of a smooth tyre could be accurately predicted. Date: 30/04/2014 Version: (122)

62 Parry found that the texture of the contact part of the surface (rms c ) had the greatest influence on the tyre-road contact pressure distributions. Both of these parameters are significantly related to the rolling resistance measurements for these drum shell surfaces; rms c is the most strongly correlated and the results are shown in Figure 42. 2,5 rolling resistance 2 1,5 1 0,5 PIARC A B 0 0,2 0,4 0,6 0,8 1 1,2 rms c Figure 42: Rolling resistance coefficient for the three tyres PIARC (smooth), Tyre A and Tyre B, versus texture described by the contact part of the profile rms value; i.e. rms c. [Parry, 1998]. These relationships were significant at the 5 % level for the PIARC tyre, 1 % for Tyre B and 0.1 % for Tyre A; where rms c predicts 83 % of the variability in rolling resistance. It can be seen that the PIARC tyre is comparatively insensitive to rms c whereas the car tyres are more sensitive and have nearly parallel relationships. It might be expected that the rolling resistance would be related to the texture of the surfaces but, for this special range of surface types, no relationship exists between rolling resistance and the rms of the texture profile curve. It is only by determining the texture of the actual contact surface that the rolling resistance behaviour can be explained, according to Parry. Overall, it was concluded that rolling resistance: a) could be predicted from the predicted contact area and roughness of the surface in contact with the tyre, but not from wavelength characteristics (spectra of macro- and megatexture), b) was related to the shape of the texture; sharper asperities increase rolling resistance, c) was related to the aggregate size; smaller was better. Date: 30/04/2014 Version: (122)

63 3.6.3 Measurements in the Netherlands Kloosterzande In 2008 TUG was contracted to conduct measurements of RR on the various test sections of the Kloosterzande test track in southern Netherlands. This had for some years been a test field for noise measurements within the huge Dutch IPG programme. In total 40 test sections were measured, using two test tyres: the SRTT and a Continental CPC2 LI98. The tests are reported in [Lopez, 2010] and [van Blokland et al, 2008]. The correlation between RRC and rms texture depth was investigatederror! Reference source not found.. Boere validated a tyre/road interaction model for the prediction of rolling resistance with the on-site measurements. He concluded that based on the measured results one can state that a conventional surface with 1 mm less rms texture depth gives a rolling resistance reduction of about 7,5 to 10 % [Boere, 2009]. These results and conclusions should be handled with care as the authors think that the texture values on which the research was based are questionable as the used laser profilometer was not working according to standards Dutch primary and secondary roads In the frame of the Dutch study [Hooghwerff, 2013], as introduced in 3.5.9, also relations between rms and RRC were studied. An overview of all rms values is given in Figure 43. Date: 30/04/2014 Version: (122)

64 Figure 43: Rms per measurement run [Hooghwerff, 2013]. Only a weak correlation (R² = 0.53) has been found by simple linear regression: Note that RRC is expressed in kg/t. Multiple regression yielded following equation with good correlation (R² = 0.71): Measurements in Minnesota In the frame of the reserach in Minnesota, introduced in section 3.3.4, following regression analyses yielded significant correlation results with rms as a texture variable: texture roughness texture texture texture texture roughness Date: 30/04/2014 Version: (122)

65 However none of these results ended up in the top ten per pavement grouping so no more details were reported in [Sohaney, 2013] Overview While certain studies from the 1990s report fair to good correlations with rms values other research reports about no relation between texture and rms. Recent investigations in the Netherlands reveal only a weak correlation. Better correlations can be found in regression analyses with more texture variables, e. g. in the Netherlands and in Minnesota. Note that for both research projects the data set consisted of mainly negative oriented textures. 3.7 Skewness Pilot study MIRIAM The Mean Profile Depth, MPD, defined in ISO (see Chapter 3 of the main text) is a measure where the peak values occurring in the segments have a more important weight than the valleys. In this way, the MPD value is already a measure which is sensitive to the asymmetry of the profile. It may at first be expected that the MPD is well correlated with the skewness as it is sensitive to the asymmetry. However, the experience so far does not verify this expectation. In Figure 44 the skewness and the MPD values of the IFSTTAR test tracks in Nantes, which were used in the RRT study [Bergiers et al, 2011] are plotted against each other. The two parameters appear to have no correlation at all for this data set. It may be concluded that MPD is not fully describing the asymmetry of the profile curve. 1 0,8 0,6 0,4 Skewness [-] 0,2 0-0,2-0,4-0,6-0,8 y = 0,1578x - 0,606 R 2 = 0, ,5 1 1,5 2 2,5 3 MPD [mm] Figure 44: Skewness versus MPD for the IFSTTAR test tracks in Nantes, France [Sandberg et al., 2011] Date: 30/04/2014 Version: (122)

66 3.7.2 Measurements in Sweden In the summer of 2011 Ulf Sandberg made an experiment to create a road surface with an extreme skewness an extremely "negative-textured" surface [Sandberg et al., 2011]. The basis was a doublelayer porous asphalt with 11 mm max chippings in the top layer, laid on motorway E4 through Huskvarna in Sweden. It was one year old at the time of the experiment. A 60 m long and 0.6 m wide section of this porous asphalt was polished by rotating discs in a machine supplied by HTC Sweden AB in Söderköping, Sweden. Approximately 1-2 mm from the peaks in the texture was polished off, leaving a flat surface of each major chipping facing upwards. After this procedure the surface was cleaned by a very strong vacuum cleaner. Later, rolling resistance was measured by the TUG trailer, using three different tyres and speeds of 50 and 80 km/h (posted speed on the site is 90 km/h). Since the polished section was only 60 m long, and maximum 50 m could be utilized for the measurements, as many as 10 runs were made for each tyre/speed combination. The results are shown in Figure 45. They show that creating a flat surface for the tyres to roll on an extremely negative skewness - is very important in order to reduce rolling resistance in an optimal way. The polishing is expected to be possible to make at a cost reasonable enough to polish road surfaces at a large scale. Date: 30/04/2014 Version: (122)

67 0,018 0,016 0,014 Rolling resistance coefficien 0,012 0,010 0,008 0,006 SRTT - Surf polished MCPR - Surf polished SRTT - Surf not polished MCPR - Surf not polished 0,004 AAV4 - Surf polished AAV4 - Not polished 0,002 0, Speed [km/h] Figure 45: Results of the rolling resistance measurements comparing the polished surface with the unpolished surface, fort he three tyres and two speeds [Sandberg et al., 2011] Measurements in Minnesota In the frame of the research that took place in Minnesota and that already was introduced in section 3.3.4, the relation between CRR and skewness (longitudinal and transversal) was studied [Sohaney, 2013]. For most of the road surfaces the texture was negative oriented. PCC pavements had larger negative skewness than asphalt pavements. Regression analyses using a single texture variable and RRC as the dependent variable lead to a significant correlation with longitudinal skewness. For grind PCC pavements correlations of 0.81, 0.80 and 0.78 were obtained for 10 th, 50 th and 90 th percentile longitudinal skewness respectively. The corresponding equations are shown here: Combinations of texture and roughness variables yielded R² values of 0.85 to 0.87 for grind PCC pavements (roughness variables 0.1 and 0.16 m; texture variables 50 th and 90 th percentile Date: 30/04/2014 Version: (122)

68 longitudinal skewness) and 0.68 to 0.73 for not ground PCC (roughness variables 1.25 and 0.63 m; texture variables 10 th, 50 th and 90 th percentile transversal skewness). Regression analyses of texture and friction lead to high correlations of 0.85 and 0.87 for 90 th and 10 th percentile longitudinal skewness in combination with friction parameter Grip Tester 10 and Ribbed tyre 11 respectively. Regression analyses using a combination of two texture variables showed R² values of 0.70 to 0.72 for the 50 th percentile longitudinal skewness in combination with various texture waveband variables for non-grind PCC pavements. The highest correlations were found for the 50 th percentile transversal skewness of grind PCC pavements in combination with 40 mm, 25 mm and 31.5 mm texture waveband, namely 0.90, 0.89 and 0.89 respectively. Combinations of texture, roughness and friction variables generated an excellent correlation of 0.93 for grind PCC pavements by using the combination longitudinal skewness, 3.15 m roughness and HFT 12. For non-grind PCC pavements correlations of 0.75 to 0.79 were found for the 50 th percentile transversal skewness in combination with various roughness and friction variables. Regression analyses using combinations of two texture variables and one roughness variable yielded excellent correlations of 0.92 to 0.93 for the grind PCC group by using transversal skewness and various texture wavebands and roughness variables. The non-grind PCC group lead to correlations of 0.77 to 0.78 with transversal skewness. The equations that were selected based on prioritising simpler and common concepts are shown in section For PCC pavements these equations include a skewness parameter, namely the 50 th percentile transversal skewness Measurements in the Netherlands In the frame of the Dutch study [Hoogwerff, 2013] addidtional to the relations with MPD (see 3.5.9) and rms (see ), also relations with skewness have been investigated. The data set contained only surfaces with negative skewness, except for three road sections. The best correlations were found with other texture parameters. In order to give a clear overview, also the results of skewness are presented here. An overview of the skewness values of all road sections is given in Figure Friction number measured by a Findlay Irving Grip Tester, a continuous friction measuring device. 11 Friction numbers measured by locked wheel testing using a ribbed tyre. 12 Friction number measured by a Dynatest 6875H Highway Friction Tester (HFT), a continuous friction measuring device. Date: 30/04/2014 Version: (122)

69 Figure 46: Skewness per measurement run [Hooghwerff, 2013]. No correlation has been found with simple linear regression. Correlations of 0.68 and 0.69 have been found with multiple regression analyses including MPD and rms. The equation with the best correlation is given here: Note that RRC is expressed in kg/t. Combinations with only rms yielded a correlation of 0.65: Summary for the influence of macro- and megatexture on rolling resistance MPD is not fully describing the asymmetry of the profile curve. Therefore skewness is of importance. Swedish research shows the impact of skewness as a lower rolling resistance was measured after a surface was polished (negative skewness). Research in Minnesota shows good to excellent correlations with skewness parameters. The last study in the Netherlands shows better correlations with other parameters, although some correlation was found with skewness. Note that in both research projects most of the road surfaces had negative skewness. Unfortunately the available research regarding correlations with skewness is limited. Date: 30/04/2014 Version: (122)

70 4 The influence of the unevenness on the rolling resistance 4.1 Introduction This section deals with the resistance caused by the unevenness of the road. Unevenness is a term used for wave components of a longitudinal trajectory of the road usually in the wavelength range 0.50 m 50 m. The most frequent statistical measure used to describe road unevenness is IRI, see [Sayers et al, 1986]. Energy losses from macrotexture essentially take place in the tyres and only to a lesser degree in shock absorbers. Hence, rolling resistance caused by macrotexture can essentially be considered as a phenomenon localized to the tyre. This is in great contradistinction to unevenness which affects the whole vehicle and gives rise to energy losses both in the tyres and in the rest of the vehicle including the shock absorbers. Probably the tyre effect (N/vehicle mass) is depending on the vehicle characteristics, and vice versa. This makes unevenness effects more complicated to study than macrotexture effects. Another important difference between unevenness and macrotexture effects is the velocity dependence. Although drum measurements indicate that the macrotexture effect varies somewhat with the velocity, this dependence seems to be rather small. In contrast, coastdown measurements indicate that the unevenness effect varies a lot with velocity, see [Karlsson at al, 2011]. Unevenness effects due to very long waves up to 50 m should not be confused with gradient effects due to the slopes. The long waves can give rise to both types of effects: the gradient one being of static nature while the unevenness effects are related to the dynamics of the movement of the vehicle. 4.2 Measures for road unevenness Road unevenness includes wave lengths in the interval m. The IRI measure has a lower wave length limit of 0.25 m, i.e. a limit below the limit included in the definition of unevenness. The contribution to IRI from the wave length interval should be most marginal which means that the contribution from the megatexture interval will be of little importance. The IRI measure was originally defined for purposes of driving comfort, but has also been used as an explanatory variable in rolling resistance models. Alternative common quantities to characterize unevenness are RMS measures for different wave length intervals. The demand for an effective road surface measure is that it should be useful in road planning matters. The definition of effectiveness in this sense should be cost effectiveness. In order to be cost effective one needs a measure with a high degree of explanation for society costs. Society costs include costs for: accidents, travel time, vehicle costs including energy etc. In this study energy costs are in focus. In the ECRPD project [Hammarström et al, 2008] the IRI measure was compared to RMS measures in different wavelength intervals. The IRI measure gave the highest degree of explanation for driving resistance. If IRI is calculated for a sinusoidal profile one can notice that IRI increases when the wave length decreases for fixed amplitude. This increase continues down to a wavelength of approximately 2 m, see [Hammarström, 2000]. Simulation of damping losses in tyres and shock absorbers continue to increase when the wave length decreases below 2 m. This raises the question if other measures Date: 30/04/2014 Version: (122)

71 without this drawback could be developed. At present IRI is the most effective roughness measure available. In Sweden parts of the national road network is measured by RST vehicles, see [Arnberg et al, 1991], each year on commission of the Transport Administration. The unevenness profile is measured in three tracks with a distance of 1.5 m and 0.25 m respectively. The database available for road planning just presents IRI for one track per 20 m road section. The variation of IRI across the road, see [Lundberg and Sjögren, 2012], is a problem both for rolling resistance measurements in real world conditions and for the use of IRI in road planning situations. Light and heavy road vehicles are exposed for different road unevenness conditions on the same road section. To what extent results in literature about rolling resistance and road roughness are based on real exposure or some value on the test section could be of importance. 4.3 Methods for estimating the unevenness contribution to the rolling resistance In general There are in principle two ways for estimating the rolling resistance contributions from road unevenness: measurement and simulation. Measurements can be split into: laboratory and real world conditions. At least in theory laboratory measurements could supply the possibility to just measure rolling resistance including a predetermined number of variables for example: the flat surface and the contribution from one wavelength interval. Laboratory conditions could include both indoor and outdoor measurements. For example outdoor laboratory measurements could include measurements on specially prepared test sites. Specially constructed straight and horizontal test sites indoor or outdoor should provide the highest probability for the possibility to isolate the contribution of different wavelengths to the rolling resistance. The different types of force measurements are possible to classify into three main groups: Wheel force measurements by stationary equipment or in a vehicle Total vehicle force measurements Total vehicle force measurements by means of energy use measurements. There are different alternatives for measurement of the total vehicle force: A test vehicle towed by another vehicle Torque measurements on the driving wheel Coast down measurements. At least for outdoor test sites all types of force measurements in principal should be possible to use. Real world measurements in general will include a main problem: to separate rolling resistance from the other types of driving resistances. Finally there is a problem to split rolling resistance into: the flat surface resistance, the macro texture resistance and the unevenness resistance. By measurements in laboratory conditions the possibility to separate roughness rolling resistance will increase but still there might be an adjustment problem. One main question about roughness resistance is if alternatives not including a complete vehicle are of main interest. If just the wheel effect is included the total vehicle roughness effect will be underestimated. One alternative could be to divide the total roughness effect into a wheel part and a part representing the rest of the vehicle. The rest of the vehicle could beside the shock absorber Date: 30/04/2014 Version: (122)

72 effect include other damping effects in the vehicle body including the suspension, an air resistance effect etc. 13 By use of wheel force equipment one part of the resistance can be estimated and expressed in a statistical model. This force then only may include the wheel effect and not the suspension effects for the test wheel in the trailer. The wheel effect neither shall include side force nor air resistance effects Force measurements Wheel force measurements by stationary equipment A typical application for this alternative should be drum measurements. By making drums somewhat elliptical it is possible to investigate the influence of wave lengths in the unevenness interval on rolling resistance. A limitation with this method is that it is not possible to generate waves with lengths above the circumference of the drum. At TUG in Gdansk, plans exist to create a surface generating waves of length 80 cm. A drawback with drums is the need for force adjustments to a flat surface. One cannot assume the standardized adjustment for drum measurements is valid also for the unevenness part of rolling resistance Wheel force measurements in a trailer wagon The test wheel is mounted in a special trailer. A new trailer wagon at TUG is currently being under development, equipped with a facility to estimate road unevenness. The measure will not be IRI, but it is expected/hoped that the new measure will correlate well with IRI. In this way, the new wagon should be able to measure the effect of both the macrotexture and the unevenness on rolling resistance Wheel force measurements in a standard road vehicle It is possible to mount force measurement equipment in the suspension for non-driving wheels A test vehicle towed by another vehicle An alternative to just measure the force of one wheel in a trailer is to measure the force for towing a complete vehicle. An advantage with this alternative compared to measurements for just one wheel is that the total unevenness effect on the vehicle will be included. One disadvantage should be how to avoid the air resistance. The towed vehicle needs to be inside a cabin Torque measurements on the driving wheel The total driving force is possible to estimate by torque measurements on the driving wheel axle. The estimated force will include a mix of driving wheel and free rolling wheel effects. 13 Road roughness will influence the vehicle body position. A change in position, distance to the road surface and attitude angle, will change the resulting air resistance coefficient. Date: 30/04/2014 Version: (122)

73 Coast down measurements The coast down driving pattern is an expression for all forces acting on the test vehicle: air resistance; rolling resistance; transmission resistance; toe in resistance; side force resistance; gradient resistance etc. One advantage of the coast down method is that the total driving resistance on an entire vehicle is measured in a reasonably natural state 14. Consequently the influence of all wavelengths of the road on the vehicle movement is detected during the measurements. So any coast down measurement will include both the effect of unevenness, as well as any other road surface property in a reasonably realistic manner. This property makes the coast down method a potentially strong candidate for measuring the driving resistance due to unevenness. Another advantage is the possibility to make measurements for any vehicle and any tyre dimension i.e. not only tyres in a limited size interval Energy use measurements One type of method frequently used to estimate road surface effects on rolling resistance is by measurements of energy use for propulsion. Fuel consumption (FC) measurements should be the most common method so far in this group. The measured fuel consumption is a function of: The external driving resistance Transmission losses Auxiliaries losses The engine efficiency. The accuracy of this type of measurements and the possibility to repeat a measurement is influenced by: Transmission losses depends on gear position, oil used and temperature Auxiliaries losses depends on temperature conditions (cooling fan), driving conditions etc. The engine efficiency depends on fuel used, oil used, temperature conditions, the stability of the engine combustion control etc. Measurements of road surface effects by fuel consumption measurements have both advantages and drawbacks. Advantages: In most cases fuel consumption is the effect asked for Both the free rolling and the drive axle effect. Disadvantages: Only valid for the measuring vehicle Only valid for the driving cycle used Only valid for the gear position used 14 There is, however, one component missing during coastdowns: the effect of the propelling force from the driving wheels. Date: 30/04/2014 Version: (122)

74 The engine decreases the level of accuracy by changing efficiency from engine temperature, meteorological conditions, time from maintenance etc. The fuel quality might change from one filling up of the tank to the next introducing systematic errors Like all road measurements there will be problems to isolate the influence of separate variables like different road surface measures. In order to make results from FC measurements more useful it is common to use constant values when multiplied with the fuel effect between two road surfaces estimating the rolling resistance effect. Since such a constant is a function of a lot of variables one conclusion should be that this type of simplified estimations should be avoided. In principle the FC advantages and disadvantages are valid for all energy use measurements How to isolate the roughness effect on RR For all measuring methods there is a problem in isolating the roughness effect on RR. By means of laboratory conditions the possibility to isolate the effect should increase. In the real world measurements one needs to include most of the following variables in the analysis: roughness macro texture megatexture cross fall radius gradient speed dv/dt meteorological conditions Even for measurements at constant speed dv/dt should be included since the possibility to keep dv/dt equal to zero is low. The same comment is valid for the gradient, i.e. test sites with gradient equal to zero are difficult to find. This can be of importance also for laboratory conditions. In principle there is no difference between measuring methods including trailer or standard road vehicle in the need to separate different parts of the total resistance measures. One needs to separate the roughness effect. The traditional way to do this is to design a function including different parts possible to isolate. The usual procedure to separate road surface forces acting on the vehicle from air drag forces is to use the fact that their velocity dependencies differ. Similarly, the transmission forces can, at least in principle, be separated by varying the load of the vehicle 15. Also, the driving resistance that is due to 15 It is reasonable to assume that the transmission losses for a freely rolling vehicle are independent of the load of the vehicle, while the rolling resistance varies (approximately) linearly with the vehicle load. In practice however, it has been found difficult to isolate the transmission losses with high precision (using coastdowns) since the load of the vehicle can only be varied with a relatively small amounts (for a passenger car). A further difficulty is that the oil chunk loss may be expected to have a component Date: 30/04/2014 Version: (122)

75 side forces acting on the vehicle may be isolated by their special dependency on velocity, cross fall and curvature. Compensation for longitudinal slope is best done by careful measurements of the geometry (altitude curve) of the test site 16. Of special interest is the separation of the effect of macrotexture, unevenness and possibly also megatexture. An obvious strategy for achieving this aim is to make measurements on various road strips having low correlation between these quantities (see [ref 1,2,3]). Unfortunately, search for suitable test sites in real world conditions is complicated by in general a rather high correlation exists between macrotexture, megatexture and unevenness. Ideally, the test sites should uniformly span a rectangle in the unvenness-macrotexture plan. Figure Unfortunately, this requirement is in sharp conflict with force measurements in real world conditions that they are vulnerable to all sorts of meteorological and other conditions during the measurements. From this point of view it would be preferable that the test sites are located close to one another in order to make measurements under approximately equal conditions. If the test sites are located far from one another so that measurements are made maybe in different days at different test sites, it may be difficult to fully compensate for the differing weather conditions, although much effort has been put in trying to control and monitor the conditions during measurements. One very easily obtains various kinds of correlations between meteorological conditions such as temperature and road surface properties such as macro texture. Such correlations may jeopardize the final results. The possibility to isolate significant road roughness effects based on FC measurements is expected to be lower compared to force measurements because of additional uncertainty caused by the vehicle propulsion system Simulation A successful simulation of driving resistance contributions from road unevenness demands a representative theoretical model. There is different type of models on different level of detail for example: Most detailed tyre models expressed by finite element method, see for example [Fraggstedt, 2006] More simplified models for the complete vehicle. Because of lacking experience of the more detailed tyre models this section gives focus on the models for the complete vehicle. These models might be split into: Quarter car models Half vehicle models linear to the velocity. This may be difficult to separate from the rolling resistance. Loss in bearings is a further component that is best compensated for in a more ad hoc manner. 16 Theoretically, it would be possible to use coastdown measurements Date: 30/04/2014 Version: (122)

76 Full vehicle models. One example on the first point is the model used for IRI estimation. One alternative for the second point is: One front and one rear axle Masses: one sprung and two unsprung Moment of inertia of the sprung mass (the moment for rotations around an axle across the vehicle) Suspensions in the front and in the rear described by spring and shock absorber characteristics Tyres described by a spring and a damper. The wheels on both side of the vehicle use the same road surface profile, i.e. there are no turning movements around the vehicle length axle. The model works like a motorcycle. This type of model has been included in the VETO program [Hammarström and Karlsson, 1987]. Based on the damping losses a roughness force is added to the total driving resistance for FC calculation. A full vehicle model, different road surface profiles on the left and right hand side, also describes the turning movements around the length axle, i.e. there also is need for the moment of inertia for the sprung mass around the vehicle length axle. In the ECRPD-project, see [Hammarström at al, 2008], VETO simulated additional resistance from roughness was compared with estimated values from coast down measurements. Measurements and simulations were based on the same roughness profiles, the same speed interval and the same vehicle. The simulated roughness resistance related the damping losses in the tyres and the shock absorbers. Simulated additional driving resistance at two constant speed levels for a test vehicle used for coast down measurements is presented in Figure 47. Date: 30/04/2014 Version: (122)

77 Roughness resistance(n) IRI 50 km/h 90 km/h Figure 47 Additional driving resistance for a test vehicle, a car, calculated by VETO (Hammarström at al., 2008). In order to compare simulations with coast down results we use the estimated IRI part of the function from coast down: dcr(iri,v) = (C 2 x IRI + C 3 x IRI x (V 20)) V: vehicle speed (m/s) In Table 5 parameters based on coast down and VETO simulations are presented. The presentation includes both total vehicle and a split into tyres and shock absorbers. Table 5 Estimated parameter values based on measurements and VETO simulations.* Parameter Coast Simulation down Tyres Shock abs. Total veh. C C R ** *After division by 1741 kg; **The total driving resistance. From Table 5 one notices that the absolute values of the simulation parameters are much lower than the parameters based on measurements. For the simulations, the driving resistance contribution from the shock absorbers is, on average, 62 % and from the tyres 38 %. The tyre proportion decreases with speed. Date: 30/04/2014 Version: (122)

78 The absolute difference between coastdown and simulations is large, the simulated resistance is far below the coastdown resistance. However there is a strong correlation between the two data sets. The conclusion should be that the vehicle dynamic part of VETO would be useful for predictions if a calibration is added. Another use of simulated values could be for applying an effective form of the roughness resistance function. 4.4 A general RR model for dry road conditions Measurements of roughness RR is of resource reasons only possible to perform for a minor part of tyre dimensions and models. For road planning there is need for a model representative for all tyres and vehicles on the road. Other representative problems are: differences between free rolling wheels and driving wheels tyre pressure tyre temperature tyre wear. A most useful support in order to develop a general RR model should be to have access to an in principal representative model including both external variables and vehicle parameters of importance for RR. By means of such a model it should be possible to develop a representative RR model for road roughness with a more limited budget. 4.5 Some results and experiences from previous coast down studies VTI has carried out coast down measurements in projects with the aim to estimate both the macrotexture and the unevenness effect on rolling resistance. In Table 6 we summarize the vehicles that were used in three of these projects. Table 6: Vehicles used for coast down measurements in several projects VTI participated Project Vehicles used Year Reference ECRPD PCar, Van, Truck without a trailer [Hammarström et al, 2008] 60 ton Truck with and without a trailer 2009 [Hammarström et al, 2012] ProdMät (SRA) PCar, Truck 2009 and 2010 [Karlsson et al, without a trailer 2011] Date: 30/04/2014 Version: (122)

79 5 Road surface texture and skid resistance 5.1 Background Definitions Even if the term skid resistance is widely use in the literature, there is still no universally accepted definition. Actually, two terms friction and skid resistance are used interchangeably and this can sometimes lead to confusion. In this chapter, the following definitions, used in the TYROSAFE project s deliverable D10 [Kane and Scharnigg, 2009], are adopted to distinguish between the meanings of friction and skid resistance. Friction, in the context of tyres and roads, represents the grip developed by a particular tyre on a particular road surface at a particular time. The coefficient of friction is a measure of this, defined as the ratio of the load (the force applied in the vertical direction) to the traction (the force resisting movement in the horizontal direction). Friction is influenced by a large number of parameters relating to the road and the tyre but it is also affected by other influences that may not be directly attributable to them, such as the vehicle suspension, ambient conditions, speed and the presence of localized contaminants (including water). Skid resistance describes the contribution that the road makes to tyre/road friction. Essentially, it is a measurement of friction obtained under specified, standardized conditions, generally chosen to fix the values of many of the potential variable factors so that the contribution that the road provides to tyre/road friction can be isolated. Unless indicated otherwise, the term skid resistance applies to wet roads and measurements are made on a wetted surface Tyre/road interface Research on road skid resistance aims at reducing accidents on contaminated roads (by water, snow, ice, etc.). This chapter focuses only on wet road skid resistance, which involves three mechanisms [Savkoor, 1990]: Slips at the tyre/road interface. Lubrication at the tyre/road interface. Generation of friction forces Slips at the tyre/road interface No friction force can be generated without any relative slip between the tyre and the road. In case of locked-wheel braking, the existence of relative slip the term skidding is used instead is easy to imagine. For a rolling tyre, no perceptible micro-slips are produced in the tyre/road contact area even if the contact patch is immobile. For braking tyre, the tyre slips on the road to compensate the fact that the rolling speed of the tyre is instantaneously below the vehicle speed due to a decrease of Date: 30/04/2014 Version: (122)

80 the angular speed of the wheel. The wheel slip (G) also called slip ratio is a measurement of the difference vehicle and rolling tyre speeds; G is given by the following formula: (1) G = V ωr V where ω is the angular speed of the wheel; R is the tyre radius; ωr is the rolling tyre speed; and V is the vehicle speed. (In some publications, G is represented as a percentage varying between 0% free rolling tyre and 100% locked tyre.) Lubrication at the tyre/road interface The tyre/wet road contact area is usually divided into three zones [Moore, 1975a] (Figure 48): in front of the tyre (zone 1), water accumulates and tends to lift the tyre; in zone 2, bulk water is evacuated and the water film becomes thin but still prevents contact between the tyre and the road; in zone 3, contact is established between the tyre and the road surface. Friction forces is generated in this zone. Figure 48 Tyre/wet road contact area Wet road friction is lower than dry road friction as the zone 3 where friction is generated represents only a fraction of the contact area. When the vehicle speed increases, the time available to evacuate water decreases. As a consequence, the size of zones 1 and 2 increases and then friction decreases. In case where zone 1 occupies the whole contact area, there is no more contact between the tyre and the road; this is the onset of aquaplaning Generation of friction forces When contact is established between the tyre and the road surface, the tyre tread is deformed by the road surface asperities (0). The sum of contact pressures gives rise to a vertical component which equilibrates the normal load and a horizontal component which resists to the movement of the tyre Date: 30/04/2014 Version: (122)

81 (0); this phenomenon, rendered possible by the viscoelasticity of the tyre rubber, is known as hysteresis. When the road surface is dry, molecular liaisons are developed between the tyre and the road. The creation and rupture of these liaisons are at the origin of horizontal forces resisting to the movement of the tyre (0); this phenomenon is known as adhesion. Figure 49 Hysteresis and adhesion components of tyre/road friction [Hall et al., 2009] Hysteresis and adhesion are often considered as distinct processes: the first one involves volume of materials, whereas the second is a surface-related phenomenon. [Kummer and Meyer, 1966] prove that hysteresis and adhesion are actually the same phenomenon energy dissipation occurring at different scales. The relative importance of hysteresis and adhesion depends on many factors of which the most important are the tyre rubber properties, the wetness and the texture of the road surface. On contaminated road surfaces, molecular liaisons are negligible and the tyre/road friction is assumed to depend on the hysteresis part only Role of road surface texture Skid resistance results from the contact between the tyre and the road, and depends on the following factors: Tyre characteristics (size, width, tread depth, rubber, etc.) ; Road surface characteristics (texture, bitumen, etc.) ; Contact conditions (wheel speed, slip ratio, normal load, etc.) ; Presence of contaminants at the interface (water, snow, ice, etc.). Date: 30/04/2014 Version: (122)

82 In this chapter, only the role of road surface texture is highlighted. Road surface texture is provided by fine sand and coarse aggregates and the compaction mode. It is composed by asperities of different sizes separated into two scales: the macrotexture and the microtexture. Macrotexture (Figure 50) is defined as surface irregularities whose dimensions are between 0.1 mm and 20 mm vertically, and between 0.5 mm and 50 mm horizontally (ISO, 1997). Microtexture (Figure 50) is defined as surface irregularities whose dimensions are between mm and 0.5 mm vertically, and below 0.5 mm horizontally [ISO, 1997]. Macrotexture 0,1-20 mm 0,5-50 mm Microtexture 0,001-0,5 mm < 0,5 mm Figure 50 Road surface texture scales [Sandberg, 1998] In the contact area the macrotexture helps, jointly with the tyre tread depth, to evacuate bulk water in zone 1 (thickness of the order of millimeter). Drainage is possible thanks to the reservoir network created by the space between the aggregates (Figure 51). Figure 51 Cartography of a road surface (80 mm 80 mm) Once bulk water is evacuated by the macrotexture, it remains a thin water film (thickness of the order of tenth of millimeter or less) in zone 2 and, locally in zone 3, water pockets at the summit of the asperities. The squeezing of these residual water films is only possible by high pressure exerted Date: 30/04/2014 Version: (122)

83 by angular asperities forming the microtexture. Friction forces are then generated by the interaction between the tyre rubber and the road surface microtexture. From the description of tyre/road interface ( ), it can be written: (2) µ = µ 0 A 1 A + A A 3 = µ 0 1 A 1 A1 + A2 + A + A where A i (i = 1, 2, 3) is the size of zone (i) in the tyre/road contact area; and µ0 is the friction coefficient that would be found on dry road surface. The term (A 1 + A 2 )/(A 1 + A 2 +A 3 ) is the fraction of the contact area occupied by water ; it summarizes all hydrodynamic actions that facilitate water intrusion and water drainage. Formula (2) can be rewritten under the following form [Veith, 1983]: (3) µ = µ 0 (1 hydrodynamic term) Formula (3) shows clearly the effect of microtexture and macrotexture on skid resistance: - the macrotexture acts on the hydrodynamic term in interaction with the speed, the water depth and the tyre tread depth; - the microtexture acts on the (µ0) term in interaction with the water depth and the tyre rubber. Even if the water depth appears twice, the order of magnitude is not the same: the macrotexture interacts with thick water depths (above 1 mm) and the microtexture with thin water depths (below 1 mm and down to hundredth of millimeter). Thin water depths remain at the tyre/road interface unless the microtexture is aggressive enough to squeeze them out. As a matter of fact, microtexture is needed at all speeds, unlike macrotexture which is needed mainly at high speed; the graph in Figure 52 actually shows that the influence range of microtexture is much wider than that of macrotexture. 2 3 Date: 30/04/2014 Version: (122)

84 Figure 52 Variation of friction coefficient with speed and the influence range of road surface macro and microtexture [Sandberg, 1998] Despite the fact that both texture scales influence friction, the macrotexture has been receiving more attention from researchers. Many reasons can be put forward to explain this tendency: research on skid resistance had been first conducted to solve the aquaplaning issue which involves principally the macrotexture. Devices and tools have been consequently developed to measure and characterize the macrotexture, and standards defined to harmonize the practices. in the meantime, despite significant progress achieved in the field of surface measurement, there is still no device that can measure the microtexture in a satisfactory way, mainly on trafficked roads. This drawback limits the development of dedicated measuring equipments and the characterization of the microtexture. For the reasons exposed above, the next two sections do not follow the same structure: section 5.2 dedicated to the macrotexture briefly summarizes the current state of knowledge and reviews parameters other than the normalized ones (for instance the Mean Profile Depth) that are potentially interesting to characterize the macrotexture; Date: 30/04/2014 Version: (122)

85 section 5.3 dedicated to the microtexture goes rather upstream to better understand the involved mechanisms which help in turn to determine the relevant characterization method. Progress in terms of measurement techniques is also stated. 5.2 Macrotexture and skid resistance Current state of knowledge In this section, main findings of research started more than fifty years ago on the effect of macrotexture on skid resistance are summarized. Key references on the topic can be found in [Kummer and Meyer, 1960], [Sabey et al. 1970], [Wambold et al., 1982], [Wambold et al., 1995], [Sandberg, 1998] and [Hall et al., 2009]. (As this report does not aim at providing an exhaustive state of the art, one key reference per decade is provided to illustrate the progress of conducted research.) Characterization of the macrotexture With respect to the drainage capacity, the macrotexture is usually characterized by two parameters: the Mean Texture Depth (MTD) and the Mean Profile Depth (MPD), defined respectively as: (4) MTD = 4V / πd 2 [ISO, 2006] where MTD is expressed in mm; V is the sample volume expressed in cubic millimeters (mm 3 ); and D is the average diameter of the area covered by the material, expressed in millimeters (mm). (5) MPD = Mean peak level Average level [ISO, 1997] where Mean peak level is the average of the two peak levels determined respectively in the two halves of a profile baseline (Figure 53); and Average level (Figure 53) is the profile mean level. Figure 53 Definition of the Mean Profile Depth (MPD) [Sandberg, 1998] Date: 30/04/2014 Version: (122)

86 Formula (4) represents the ratio between the volume of solid glass spheres ( ± 150 mm 3 ) spread on the road surface to form a circle and the area of this circle (Figure 54). The measurement method, known as the patch or volumetric method, is a spot method and needs road closure to traffic. Figure 54 Definition of the Mean Texture Depth (MTD) [ISO, 2006] The MPD was originally developed to replace the MTD as it can be determined continuously along a surface at normal traffic speed. It is possible to estimate MTD the estimate is called Estimated Texture Depth (ETD) from MPD using the formula [ISO, 2006]: (6) ETD = MPD Interaction of macrotexture with other factors It is obvious that water drainage and its opposite water intrusion does not depend only on the road surface macrotexture. Water intrusion is enhanced by the water depth and the vehicle speed, whereas water drainge is facilitated by the macrotexture and the tyre tread depth. References like [Sabey, 1970], [Veith, 1983] as well as [Gothié et al., 2001] provide exhaustive experimental evidences about the interaction between these four factors. (Other factors like the normal load or Date: 30/04/2014 Version: (122)

87 the tyre width are also relevant with respect to the water issue. However, the first four factors have been the most extensively studied). The basic skid resistance representation is the graph giving the variation of friction coefficient with speed (Figure 55). It can be seen that wet skid resistance decreases with speed and the decrease rate depends strongly on the road surface macrotexture. Figure 55 Variation of friction coefficient with speed for various macro/microtexture types [Hall et al., 2009] The interaction between macrotexture and speed is well illustrated by the Penn State model [Leu and Henry, 1978]: V V (7) µ = µ 0 0 e where µ 0 is a theoretical friction coefficient at speed zero; V is the speed; and V 0 is a constant related to the road surface macrotexture. In the PIARC model (whose the general form is the same as (7)), developed after the PIARC experiments and constitutes the basis of the harmonization process of friction measurement methods (Wambold et al., 1995), V 0 is named speed constant and expressed as: (8) S p = a + bt where S p is the speed constant; T is a parameter characterizing the macrotexture (preferably the MPD but can be other measures like MTD); and (a) and (b) are constants related to the texture measuring device. (Actually, S p is not strictly constant as it depends on texture.) The original Penn State model or its more comprehensive development s PIARC model is based on the assumption that the friction-speed variation is exponential. Some experimental evidences have Date: 30/04/2014 Version: (122)

88 shown that this variation can have another form as illustrated in Figure 56a. This form is similar to that of the Stribeck curve widely used in tribology to study lubrication between two solids (Figure 56b) [Faraon, 2005]. a) b) Figure 56 Variation of friction coefficient with speed (a: graph obtained from braking tests (Gothié et al., 2001); b: Stribeck curve representing boundary (BL), mixed (ML) and elastohydrodynamic (EHL) lubrication regimes [Faraon, 2005]) Some authors suggest alternative models to better take into account this variation form [Mancosu et al, 2000]: (9) µ = µ ref ( ) b b b + b V 1+ b 3 4 2e where µ ref is a friction coefficient measured under specific conditions; V is the speed; and b i (i = 1 to 4) are constants related to the water depth and the road surface macrotexture. Even if models like the one presented in formula (9) are less popular than the Penn State model and its variants, they highlight interactions between road surface macrotexture and other factors like water depth or tyre tread depth. The graph in 0 for example shows how tyre pattern affects the variation of friction with water film thickness (WFT). For friction monitoring purpose, road authorities might be less concerned by these interactions than by the one between macrotexture Date: 30/04/2014 Version: (122)

89 and speed, as skid resistance is obtained under specific conditions (see 5.1.1) mainly at constant water depth and using blank or controlled patterned tyres. Figure 57 Variation of friction coefficient with water film thickness [Hall et al., 2009] Let us cite another comprehensive model developed by [Horne and Buhlmann, 1983] and yet not widely used for road friction monitoring: p p (10) µ = µ 1 C 1 + C 2 dry µac µic p p where µ dry is a friction coefficient measured at very low speed on a damp surface; p is the tyre inflation pressure; p 1 (resp. p 2 ) is the fluid pressure generated under zone 1 (resp. zone 2) of the tyre/road contact area; Cmac (resp. Cmic) expresses the water drainage provided by road surface macrotexture (resp. microtexture), value 0 (resp. 1) meaning perfect drainage (resp. no drainage). The Horne and Buhlmann s model, via parameters C mic and p 2, highlights the role of microtexture in the water drainage process which is rarely taken into consideration in models. One of the main drawback of Horne and Buhlmann s model is the required experiments to obtain its inputs. Actually, the measurement of pressures (p 1 ) and (p 2 ) needs a specific setup and can be done only in laboratory Other characterization methods In ISO standard (1997), it was stated that other International Standards dealing with surface profiling methods are mainly used for measuring surface finish (microtexture) of metal surfaces and were not intended to be applied to pavements. According to the author of this part of the report, even if texture of road surfaces is different from that of machined surfaces, some Date: 30/04/2014 Version: (122)

90 parameters developed for the last ones might be of interest to road engineers. As one objective of WP4 is to look for parameters other than MPD and MTD to characterize the macrotexture, some of these parameters are listed below. Standard EN ISO 4287 (1998) defines the maximum profile peak height (Rp) as the maximum distance between the mean line and the highest point within the sample (Figure 58a). The average of Rp values determined respectively on five sampling lengths is called the mean profile peak height (Rpm) (Figure 58b). If we compare Figure 58b to Figure 54, it can be said that MPD is more or less a modified Rpm (the evaluation length comprises two sampling lengths instead of five). a) b) Figure 58 Peak profile height R p (a) and Mean peak profile height R pm (b) [Zygo, 1993] The above example confirms the potential of parameters developed for machined surfaces with respect to pavement applications. As [Stout et al., 1993] mentioned, surface topography can be described by general parameters like RMS or skewness (asymmetry of height distribution) and kurtosis (sharpness of height distribution). However, the same authors also said that it is sometimes more efficient and effective to use specially designed functional parameters to describe the particular characteristics of a surface that are important for a specific functional application. With respect to road surface macrotexture, the main functions expected from these surface irregularities is the water drainage and the contact surface with the tyre. It could be then interesting to look at two well known functions of machined surfaces: the bearing and fluid retention properties. Illustration of bearing area is shown in Figure 59a. One defines bearing ratio as the ratio (expressed as a percentage) of the length of the profile at any specified depth in the evaluation length. Bearing ratio varies with depth (0% at highest point and 100% at lowest point) and the resulting graph is called the bearing area curve (Figure 59b) which is generated by simulating a horizontal line moving through the profile from the top down, evaluating the percentage of contact the line would make with the surface at each level. Date: 30/04/2014 Version: (122)

91 a) b) Figure 59 Surface bearing (a: bearing area; b: bearing area curve) From a bearing area curve, it is possible to define three other parameters (Figure 59b): Reduced Peak Height (R pk ) is the top portion of the surface that will be worn away in the running in period. The running in period can correspond to the early life of a road surface (six months to one year). Core Roughness Depth (R k ) is a measure of the core (peak to valley) roughness of the surface with the major peaks and valleys removed. As a matter of fact, this parameter may be used to replace parameters such as Rq or Ra when anomalous peaks or valleys may adversely affect the repeatability of the measures. Reduced Valley Depth (R vk ) is the lowest part of the surface that retains lubricant. Many 2D parameters have already been defined in many national and international standards. However, one can argue that the 2D parameters cannot provide adequate and reliable information for the analysis of intrinsically three dimensional surface topography, whereas 3D parameters may offer an attractive and realistic solution. The three parameters related to bearing properties (Reduced Peak Height, Core Roughness Depth, Reduced Valley Depth) can be extended to 3D case as respectively R pk, R k and R vk [Stout et al., 1993] (Figure 60). Date: 30/04/2014 Version: (122)

92 Figure 60 Characterization of Abbott curve and corresponding parts of a profile [Stout et al, 1993] Examples of rough surfaces and the related bearing area curves are shown in Figure 61. Date: 30/04/2014 Version: (122)

93 Figure 61 Rough surfaces and related bearing area curves [Stout et al, 1993] 5.3 Microtexture and skid resistance Understanding the physical phenomena Theoretical considerations It was stated in section that microtexture is needed to squeeze out the thin residual water film and generate friction forces. However, it is difficult to observe what happens at the tyre/road interface at submillimeter scale. Modeling is then a common way to explore the connection microtexture/water/friction. Knowledge from tribology is valuable as it can constitute a good basis to better understand lubrication regimes at the tyre/road interface, estimate the water film thickness, etc. Relative slip between the tyre and the road generates, in the presence of thin water films, hydrodynamic pressure which rises rapidly on the upward slope of surface asperities and then drops to negative values on the downward slope (Figure 62) [Moore, 1975a]; the result is an uplifting force which tends to separate the tyre and the road. Date: 30/04/2014 Version: (122)

94 Figure 62 Generation of hydrodynamic pressure due to relative slip between the tyre and the road [Moore, 1975] This mechanism called elastohydrodynamic separation generates a water depth h c * at asperity summits which ensures equilibrium between hydrodynamic (due to the water film) and elastic (due to rubber deformation) forces acting on the tyre tread. Modeling [Moore, 1975a] shows that h * c is greater for more rounded asperities and increases as the relative slip speed increases (Figure 63), and that the relative speed varies with position in the contact area and reaches a maximum value at the rearmost point of the traction zone (zone 3). Thus, erosion of the contact area also occurs from its rear part, in addition to bulk water intrusion from the front part ( ); contact loss due to thin water film is usually referred to as viscoplaning. The graph in Figure 63 also shows that the microtexture height at asperity summits (denoted by εmr in the figure) must be at least as high as h * c ; however, for given ε MR, there is a critical slip speed u crit above which contact is loss between the tyre and the road. Figure 63 Variation water depth with slip speed [Moore, 1975a] Date: 30/04/2014 Version: (122)

95 [Savkoor, 1990] in his theory also states the need of having a minimum microtexture height to emerge from the water film. The same author adds that, due to the high viscosity related to the low thickness of the water film (few microns), microtexture also needs good sharpness to break down the water film. [Rohde, 1976] and later [Taneerananon and Yandell, 1981] prove that the approaching time between two parallel plates which simulates the time needed for a tyre tread element to sink in the water film and touch the road surface decreases when one surface is rough and the decrease is more important for triangular asperities compared with cylindrical asperities (Figure 64). These works highlight the fact that shape is another main characteristic of road surface microtexture. Figure 64 Influence of roughness and shape on the time of sinkage [Taneerananon and Yandell, 1981] Few studies have been found on the effect of microtexture density. From their numerical simulations, [Taneerananon et Yandell, 1981] conclude that water depth is hardly affected by this parameter. Nevertheless, in a previous theoretical work, [Yandell, 1971] found that microtexture density affects hysteresis friction: the friction coefficient increases first for increasing density then, above a critical density, decreases. This author explains his observation by the dissipated energy related to rubber deformation by surface asperities. Actually, as rubber is viscoelastic, loadingunloading cycles induced by surface asperities dissipate energy; the dissipated energy generally increases when the deformation frequency increases. However, above a critical density, the distance between two consecutive asperities is not long enough to unload the rubber completely; the dissipated energy decreases and so the hysteresis friction. Date: 30/04/2014 Version: (122)

96 Experimental evidences Despite difficulties related to real surfaces (mainly random texture), research on aggregates also brings up valuable information about microtexture. [Bond et al., 1976] and [Lees et al., 1976] observe aggregates by means of SEM (scanning electron microscope) and look for correspondence between microtexture height and wet tyre/road friction. Both studies conclude that friction increases when aggregate surface relief increases between 5 µm and 100 µm. In addition, Lees et al. found that above 100 µm, friction gain is not significant whereas tyre wear becomes dominant. Experiments conducted by [Sabey, 1958] on single steel spheres and cones sliding on wet rubber substrate show that friction coefficient is higher for conical sliders and for low angles at the cone summit. This author calculate the average pressure over the contact area of the sliders (Figure 65a) and proves that friction variation can be explained by the pressure generated at the slider summit (Figure 65b). [Greenwood and Tabor, 1958] develop a theory from Sabey s results and explain that the relationship between friction and pressure is due to the fact that hysteresis dominates slider/wet rubber friction. Both publications highlight the role of road asperity sharpness to break through the lubricating water film. a) b) Figure 65 Contact of steel sliders on wet rubber (a: calculated contact pressure; b: variation of measured friction coefficient with calculated contact pressure) [Sabey, 1958] Recent works by [Do et al., 2013] provide a further insight into the variation of skid resistance with water depth, mainly for thicknesses of the order of tenth of millimeter. Using sandblasted aggregate mosaics (Figure 66a), these authors can vary the microtexture level while maintaining the same macrotexture. The friction/water depth variation is obtained by measuring the friction coefficient at dry state and different wet states by spraying the surface with successive equal quantities of water. Figure 66b shows that without any microtexture (specimen S590-E0) the friction coefficient drops as soon as the surface is wetted. With microtexture (specimens S590-E1 to E3), the friction coefficient Date: 30/04/2014 Version: (122)

97 remains close to the dry value until the water depth reaches a critical value (between 0.1 and 0.2 mm); above this critical water depth, the friction coefficient drops drastically. Increasing the microtexture level (E1 to E3 respectively) also helps to maintain higher friction coefficient. a) 1,5 friction coefficient 1,0 0,5 S590-E0 S590-E1 S590-E2 S590-E3 b) 0,0 0,0 0,2 0,4 0,6 0,8 1,0 water depth (mm) Figure 66 Variation of friction coefficient with water depth (a: sandblasting of the specimen surface ; b : comparison with/without microtexture) [Do et al., 2013] The above observations corroborate those made by [Moore, 1975b]. Graphs in Figure 67 show that microtexture helps to increase the critical speed above which friction drops rapidly. Date: 30/04/2014 Version: (122)

98 Figure 67 Variation of friction coefficietn with elastohydrodynamic number [Moore, 1975b] Microtexture measurement The scale issue Microtexture is defined [ISO, 1997] without any lower limit. Good measurement means then high resolution to miss no relevant detail. However, sensors with high resolution are also limited in terms of depth of view (distance at which the sensor still sees the surface). The main issue when one tries to measure the microtexture is the scale at which measurements must be done. The useful scale depends on the domain of interest: geologists ask for 100 µm whereas tribologist or tyre manufacturer can ask for 1-10 µm as adhesion and thin film traction involve these small scales Subjective methods Let us cite first the so-called indirect methods which consist of measuring friction coefficient at low speed (< 50 km/h); a description of these methods can be found in TYROSAFE deliverable D4 [Do and Roe, 2009]. These methods, widely used for road monitoring, are based on the assumption that microtexture effect is dominant at low speed. The Ontario Ministry of Transportation develops a classification method based on stereoscopic photography [Schonfeld, 1970; Holt & Musgrove, 1982]. Parameters are first defined to characterize macro- and microtexture (Figure 68). Date: 30/04/2014 Version: (122)

99 Figure 68 Macro- and microtexture parameters (letters A to F) defined by [Schonfeld, 1970] Then, for each parameter, marks are attributed to asperities characteristics. Figure 69 illustrates the case of coarse aggregate microtexture (class E in Figure 68); marks vary from E1 to E8 depending on microasperity angularity and height. a) b) c) d) Figure 69 Example of marks attributed to microtexture of coarse aggregates [Holt & Musgrove, 1982] Profile and cartography measurements The first works dealing with microtexture profiles [Yandell, 1971] cite the use of tactile sensor widely used for the measurement of roughness of machined surfaces. The main advantage of this type of sensors is their insensitivity to the surface aspect; artefacts are then minimized. However, tactile sensors cannot be used on every road surface as the measuring tip can be locked by surface troughs (space between aggregates for example). The use of laser sensors provides promising results [Forster, 1981; Do et al., 2000]. Due to the compromise between resolution and depth of view and the texture of road surface or aggregates, Date: 30/04/2014 Version: (122)

100 only short profiles (length of 2 mm to 5 mm) can be measured. The main drawbacks of this method are then: representivity of profile length, time consuming, small measurement area. In the field of 3D measurements, let us cite the InfiniteFocus sensor (Figure 70a) based on the focus variation principle [Danzl et al., 2011]. a) b) Figure 70 InfiniteFocus sensor (a: view of the equipment; b: principle of focus variation [Danzl et al., 2011]) The principle of focus variation is described in Figure 70b. The main component of the system is a precision optics containing various lens systems that can be equipped with different objectives, allowing measurements with different resolution. With a beam splitting mirror, light emerging from a white light source is inserted into the optical path of the system and focused onto the specimen via the objective. All rays emerging from the specimen and hitting the objective lens are bundled in the optics and gathered by a light sensitive sensor behind the beam splitting mirror. Due to the small depth of field of the optics only small regions of the object are sharply imaged. To perform a complete detection of the surface with full depth of field, the precision optics is moved vertically along the optical axis, while continuously capturing data from the surface. This means that each region of the object is sharply focused. Algorithms convert the acquired sensor data into 3D information and a true color image with full depth of field; an example is shown in Figure 71. The vertical scan range depends on the working distance of the objective and ranges from 3.2 to 22 mm. The horizontal range can be up to 100 mm 100 mm and more. Date: 30/04/2014 Version: (122)

101 Figure 71 Example of height cartography of aggregate mosaic surface measured by InfiniteFocus sensor The methods described above, despite their high performance, still present two drawbacks: they are dedicated to laboratory use and the measurement time is too high (2-3 hours for a height cartography). For outdoor use, it is necessary to develop faster methods implemented in equipment that can be transportable to the site. For this purpose, image analysis appears as a promising approach. The prototype developed by [Ben Slimane et al., 2007] (Figure 72a) uses a high-resolution camera to measure images and dedicated algorithms to extract roughness information from images. The procedure separating relief from aspect-information using a photometric model for the surface is detailed in [Ben Slimane et al., 2007]. Examples of results are shown in Figure 72b. a) Date: 30/04/2014 Version: (122)

102 b) Figure 72 Image-based system (a: view of the prototype; b: examples of road surfaces (left) and the height cartography extracted from images (right)) [Ben Slimane et al., 2007] Microtexture characterization Review of the connection microtexture/water/friction (5.3.1) highlights three main types of parameters to characterize road surface microtexture with respect to skid resistance: height, shape and density Need for a local description Standardized Rq (root-mean-square or RMS) is used by [Savkoor, 1990] to describe microtexture and define the delubrication criteria of the elastohydrodynamic film, i.e., * h c < 1 (see ). Rq [Do et al., 2013] plot the variation of friction coefficient with microtexture RMS for dry and wet surfaces. Figure 73 shows that friction decreases with increasing RMS on dry surface and, inversely, increases with increasing RMS on wet surfaces. The tendency on dry surface can be explained by the fact that dry friction depends on available contact area, which decreases as the surface becomes rougher (increasing RMS). The tendency on wet surfaces confirm what is said in previous sections about delubrication mechanisms ( ). In addition, it can be seen that friction increases sharply above a RMS threshold (roughly 6 µm for 0.3 and 0.5 mm in water depth) meaning that direct contact between is established between the tyre and the road; for 1 mm, no sharp increase is observed meaning that many asperities are still masked by the water film. Date: 30/04/2014 Version: (122)

103 Figure 73 Variation of friction coefficient with microtexture for dry and wet surfaces (Do et al., 2013) Height parameter like R q can be used to assess (de)lubrication mechanisms. However, for a prediction of skid resistance, parameters allowing the calculation of friction forces are needed. Due to local presence of water pockets at asperity summits (5.1.3), it is logical that a relevant microtexture description must be local too. Few authors have dealt with local description of microtexture. [Forster, 1981] defines summits and valleys on a profile as local maximum and minimum points respectively; the number of summits can be used to define the density. This author defines then the height and shape parameters as illustrated in Figure 74. Note that the shape parameter is more or less the cotangent of the angle at asperity summit which is proportional to the contact pressure in Sabey s theory (1958). Date: 30/04/2014 Version: (122)

104 a b c L a + b + c Hauteur height = 3 density Densité = 3 L a + b + c shape 3 Forme = L 3 Figure 74 Characterization of microtexture [Forster, 1981] Relevance of defined parameters is assessed by means of their correlation with friction coefficient. Forster shows that the shape factor is the most correlated factor (Figure 75). a) b) Figure 75 Correlation between microtexture parameters (a: height; b: shape) and friction coefficient measured by British Pendulum [Forster, 1981] Let us mention the curvature parameter that can be used as a local descriptor of microtexture asperities. For the time being, no study has been found to evaluate the relevance of curvature with respect to rood skid resistance. For a profile, the formula is simply [Greenwood, 1984]: (11) 1 zx x, i 2 zx, i + zx+ x, i = r 2 i x where z x,i is the height of asperity (i) located at abscissa (x); and x is the profile sampling interval. Date: 30/04/2014 Version: (122)

105 Motif combination The motif combination method was developed by and for French automobile industry. A very good description of the method can be found in [Fahl, 1982]. Motif is part of a profile between two consecutive summits (Figure 76). Definition of a motif begins by the search of summits and valleys. Simple criteria like local maximum and minimum can be used to define summits and valleys. The following parameters can be calculated from motif (i) (Figure 76): - the height R1,i between the left summit and the valley; - the height R2,i between the right summit and the valley; - the characteristics Ti, which is the minimum of R1,i and R2,i; - the mean height Ri of the motif, which is the average of R1,I and R2,i; - the width ARi of the motif, which is the distance between the summits. Figure 76 Definition of motif To suppress small summits, combinations can be done following precise rules [Fahl, 1982]. Once motifs are detected and eventually grouped, two parameters can be calculated: R: mean of Ri; AR: mean of ARi. The envelop line linking all summits is then analyzed; this line is called undulation profile. New motifs, called undulation motifs, are detected following the same rules as those applied to the original profile. Two new parameters are calculated: W: mean of height Wi of undulation motifs; AW: mean of width AWi of undulation motifs. Analysis of profiles by means of motif combination uses then two scales: roughness and undulation. Based on previous works [Forster, 1981], parameters R/AR and W/AW seem to be relevant to characterize microtexture with respect to friction. Date: 30/04/2014 Version: (122)

106 [Zahouani et al., 2000] validate the concept of motif combination using three types of surface to isolate the effect of microtexture: surfaces without macrotexture represented by rock facies obtained by crushing (Figure 77a); surfaces with constant macrotexture represented by coarse aggregate mosaics (Figure 77b); road surfaces (Figure 77c). Using this approach, one can assess the relevance of microtexture parameters and the meantime see the scatter induced by macrotexture. a) b) c) Figure 77 Surfaces used for the assessment of microtexture parameters [Zahouani et al., 2000] Correlation between R/AR, W/AW and friction coefficient (British Pendulum) for mosaics of aggregates is shown in Figure 78. It can be seen that, except for two specimens, R/AR and W/AW are fairly correlated to friction coefficient. Figure 78 Correlation between motif descriptors and friction (friction values were multiplied by 100 on the Y axis) Indentor method The motif combination method is further adapted to the context of tyre/road contact. For this purpose, indentor is defined as asperity formed by one summit and the two neighbor valleys (blue triangle in Figure 79). Even if the definition of summits and valleys as well as the criteria to detect them are the same, the triangular form of indentors materializes better surface asperities than a motif. Date: 30/04/2014 Version: (122)