Development of correlation equations between different measurements of skid resistance in pavements

Transcription

1 Indian Journal of Engineering & Materials Sciences Vol. 13, April 2006, pp Development of correlation equations between different measurements of skid resistance in pavements Marcelo Bustos, Tomas Echaveguren, Hernán de Solminihac* & Andrés Caroca Department of Construction Engineering and Management, School of Engineering, Pontificia Universidad Católica de Chile, P.O. Box 306, CP , Vicuña Mackenna 4860, Santiago, Chile Received 17 May 2004; accepted 13 September 2005 Friction is an indicator of the safety level supplied by a pavement. It is obtained by skid resistance and texture measures performed with static and high performance devices. Because the physical principle applied by each equipment differs from one another, it is necessary to harmonize measures in order to compare them. From literature review different harmonization procedures are identified: equipment direct correlations, high scale harmonization experiments and statistical analysis of skid resistance measures. Harmonization based on direct correlations and statistical analysis has the disadvantage that they do not consider texture as a variable, making them applicable only under certain conditions. In this paper, a harmonization methodology is proposed based on the PIARC (the World Road Association) harmonization procedure. The proposed methodology has the advantage that a reduced amount of data is needed and also texture measures are considered. From the application of the methodology under data simulation it is concluded that under a certain texture range the correlation is almost linear. This range depends on the texture measurement device; as, when using accurate equipments non-linear distortions are deduced under low texture levels. IPC Code: E 01C 9/10 Pavement performance can be described through structural and functional indicators. The former, characterize pavements behaviour under traffic loads. The latter consider comfort and safety level supplied to road users 1. Skid resistance and pavement texture are functional properties that provide safety conditions to drivers. Pavement friction is the combination of both variables. Skid resistance is measured with static devices such as British Pendulum or high-speed devices such as Grip Tester. Pavement texture is usually measured with volumetric methods or with laser profilers. Road agencies usually need to compare friction measurements obtained with different devices. Such measures cannot be directly compared as each equipment measures under different physical principles. As an example, SCRIM (side-force coefficient routine investigation machine) uses a test wheel with a deviation of 20 from the vehicle's trajectory under free spinning. On the other hand, Grip Tester (GT) uses a test wheel oriented in the same direction to vehicle's trajectory, but blocked in a 14.5%. This problem can be solved using methods to harmonize measures, such as the European Friction Index (EFI), statistical comparison of measures, *For correspondence ( hsolmini@ing.puc.cl) APSA Pavement Management Consultant AS or regression equations 2-4. Experiments have been carried out in the United Kingdom, New Zealand and Chile to measure and correlate friction measurements done with SCRIM, Grip Tester and British Pendulum (BP) However, they do not consider the effect of the pavement's macro texture, so their application is limited to texture values under which they were calibrated. The objective of this study is to develop an alternative procedure to obtain equations that directly correlate skid resistance measured with different devices. A mathematical procedure was developed to estimate skid resistance of one device based on measurements performed with another device. To obtain more simplified expressions, such equations were used to generate simulated friction data. With the data obtained under mathematical simulation, equations were determined under non-linear regression. Theoretical Existing correlation equations between friction measures Many highway agencies and manufacturers have developed equations to correlate skid resistance measures obtained with different devices. The United Kingdom, New Zealand and Chile, among other countries developed correlation equations between British Pendulum and SCRIM. A synthesis of these

2 118 INDIAN J ENG. MATER. SCI., APRIL 2006 equations is presented in Table 1. The equation proposed by the Highways Agency of the UK correlates SCRIM and British Pendulum measurements. It is applicable for medium textured surfaces, but not for low texture surfaces. This is because, as macro-texture is reduced, the British Pendulum Number (BPN) tends to be affected by the micro and macro-texture, resulting into a non-linear behaviour 13. In Chile this equation cannot be applied as most roads have low texture. The Transit NZ Standards suggest linear equations to correlate measurements obtained with British Pendulum and SCRIM. If the constant value of this equation is neglected, the equation predicts BPN values 35% higher than those obtained with highways Agency Equation (see Table 1). In contrast, Chilean correlation equations for British Pendulum and SCRIM predicted values 35% lower than those obtained with equation provided by the Highways Agency. However, the correlation coefficient of the Chilean expressions is low, so it is not suitable to correlate both devices. There are also some equations to correlate SCRIM and Grip Tester. All of them are linear expressions that present correlation coefficients higher than 90%. The models allow a simple estimation of skid resistance measures performed with different devices. However, they do not consider the pavement macro-texture effect, so their scope of application depends on the texture values under which they were calibrated. model considers a "reference curve". This curve is representative to all pavement surfaces and devices harmonized by PIARC. An adjustment procedure was developed for several devices to shift each particular friction curve as close as possible to the reference curve. This procedure is presented in Fig. 1. The method can be expressed mathematically by Eqs (1)-(5). Eq. (1) represents friction (FS) measurement harmonized to the reference curve. Texture is represented by the Sp constant. A unique expression is obtained for both, texture and skid resistance. [(60 S)/Sp] FS(S) = F60 e (1) The constant Sp is obtained through the linear expression presented in Eq. (2), from macro-texture measurements obtained with different methods (sandpatch method, profilometers and drainometers, among other methods). Sp = a + b Tx (2) where T x is the measure of macro-texture, and constants a and b are the PIARC coefficients for each macro-texture measurement method. Knowing the skid resistance (FRS) measured under a specific survey speed (Se) and considering the Sp value obtained from Eq. (2), the reference friction at 60 km/h (FR60) can be estimated through Eq. (3). [(Se 60)/Sp] FR60 = FRS e (3) Proposed methodology to correlate skid resistance measurements The PIARC model provides an efficient mean to overcome with some of the limitations observed in the reviewed methodology, as it considers skid resistance, texture and a harmonization procedure under different survey speeds 2. The PIARC model is based on frictionspeed model of Rado 2. This model considers an exponential decay of skid resistance against speed, as presented in Eq. (1). To harmonize measures done with different devices, the Table 1 Correlation equations between several tribometers 5-7,9-11 Fig. 1 PIARC harmonization method 2 Id Author Year Country Equation 1 Transportation & Road Research Laboratory 1990 United Kingdom SC = GN Highways Agency 1994 United Kingdom SC = BPN/ Highways Directorate 2000 Chile SC = BPN Transit NZ 2001 New Zealand ESC = BPN Transit NZ 2001 New Zealand ESC = 0.42 GN Central University of Chile 2003 Chile SC = GN Findlay Irvine Ltd 2004 United Kingdom SC = 0.85 GN GN: Grip Number; BPN: British Pendulum Number; SC: SCRIM Coefficient; ESC Equivalent SCRIM Coefficient

3 BUSTOS et al.: SKID RESISTANCE IN PAVEMENTS 119 The normalized value of skid resistance at 60 km/h (F60) is obtained with Eq. (4), valid for tribometers with blank tires or slider devices. A and B are the PIARC coefficients for a specific device. F60 = A + B FR60 (4) When equipments have ribbed tires, an additional term C T x has to be added to Eq. (4), in order to consider the incidence of micro-texture. To estimate friction values of one device from friction measurements performed with another device, it is assumed that harmonized friction measures are equal between both devices. This is presented in Eq. (5). FS(S) i = FS(S) j (5) As the e [(60 S)/Sp] term is the same for both devices, it can be eliminated from Eq. (1). Replacing the values for devices i and j in Eqs (2)-(5), and considering their survey speed values (Se i,, Se j ), Eq. (6) is obtained: [(Sei - 60)/(a + b Tx)] FRS j = [ A i + B i FRS i e A j ]/B j / e [(Sej - 60)/(a+ b Tx)] (6) With Eq. (6) specific skid resistance values can be estimated from other measures obtained with different equipments. From this equation it is clear that FRS j depends only on the variables FRS i and T x, as the remaining coefficients (A i,j, B i,j, Se i, Se j, a, b) are constant values for specific skid resistance measurement devices (i and j) and texture measurement method. With the help of numerical simulation data, it can be obtained for the determination of simplified regressions. Results and Discussion Application of the proposed methodology The procedure outlined in the previous section can be applied to devices considered for the PIARC experiment. In this study, an application was developed for the following tribometers: (i) British Pendulum, (ii) SCRIM, (iii) Grip Tester, (iv) Komatsu Skid Trailer, and (v) Stradograph. Two methods to Table 2 Parameters used for the tribometers included in this study 2 Tribometer PIARC Code Se (km/h) Pendulum Tester BPT A SCRIMTEX (UK) D Grip Tester D Komatsu Skid Trailer C Stradograph C A B measure macro-texture were also considered: Sand- Patch Test Method and Laser Profiler. The parameters used in the analysis are presented in Tables 2 and 3. For each pair of tribometers a set of friction values were generated for skid resistance values between 0.3 and 1.1 (30 to 110 to the British Pendulum), and macro-texture values between 0.35 and 2.0 mm. An example of calculated values using Eq. (6) is presented in Figs 2a-d where macro-texture was obtained with sand-patch method and profiler. The following issues can be appreciated from these figures: (i) a correlation between skid resistance values is observed, independent of macro texture for high texture values (ii) the macro texture effect over friction is not homogeneous. For low texture levels a high incidence over friction is observed, (iii) the effect of the macro-texture is clearly lower if texture is measured by a profilometer Eq. (7) analytically justifies these assumptions: (a) linear correlation between skid resistance measurements, included in the first term, (b) non-linear influence of the macro texture on the skid resistance values, considered in the second term, and (c) combined effect of skid resistance and texture over the estimation, considered through the third term FRS j = k 1 FRS i + k 2 T x k3 + k 4 FRS i T x (7) Simulated skid resistance data was generated for each pair of tribometers associated with two macro texture measuring methods. Then, a non-linear regression analysis was performed to estimate coefficients k i for each set of data. If any of the coefficients was not statistically significant at 95% confidence level, the equation was then re-estimated without such coefficient, until all of the coefficients were statistically significant. Values obtained for the coefficients are presented in Tables 4 and 5 where macro-texture was estimated from sand-patch method and profiler respectively. Comparative analysis between the proposed methodology and existent correlations Comparing existing correlations to those obtained in this study itcan be said that: Equations show in Table 1 which correlate SCRIM Table 3 Parameters used for the methods considered to measurement of macro texture 2 Equipment PIARC Code Sand-Patch Method A Laser Profilometer A a b

4 120 INDIAN J ENG. MATER. SCI., APRIL 2006 values from measurements performed with British Pendulum was compared with Eq. (8) estimated in this study. SC = BPN MPD (8) Where MPD is the Mean Profile Depth (mm) measured with profilometer. A graphical comparison of Eq. (8) with those showed from Table 1 is presented in Fig. 4. Eq. (8) has been plotted with two values of macro-texture: MPD = 0.4 mm and MPD = 2.0 mm. Results show that BPN values lower than 70 differs between equations in less than 15%. For BPN values higher than 70, a similar phenomena is observed, with the exception of the equation developed by Chilean Highways Directorate. Using these equations, values almost 40% higher than the mean of the other curves were obtained. To compare equations developed to estimate equivalent SCRIM values from Grip Tester numbers, Eq.(9) was used which was developed in this study (extracted from Table 5). SC = GN MTD MTD GN (9) Fig. 5 presents a graphical comparison between equations listed in rows 5, 6, and 7 from Table 1 and (9). Eq. (9) has been also plotted with MTD = 0.4 and MTD = 2.0 mm. In this case, the equation developed by Transit NZ, present values that are significantly lower than the values predicted by the other two equations, especially for high values of GN. Differences between 13 and 45% are observed when compared to the equations developed in this study. The values predicted by Central University of Chile and Eq. (9) are similar, presenting differences lower than 20%. Fig. 2 Tri-dimensional surfaces of SCRIM data generated from Grip Tester and from British Pendulum

5 BUSTOS et al.: SKID RESISTANCE IN PAVEMENTS 121 Table 4 Coefficients statistically estimated for Eq. (7) for texture measured with Sand-Patch Metho To device values From device values K 1 K 2 K 3 K 4 Standard error British pendulum number SCRIM number Grip number Komatsu skid number Stradograph number Grip number SCRIM number British pendulum number Komatsu skid number Stradograph number SCRIM number Grip number British pendulum number Komatsu skid number Stradograph number Komatsu skid number SCRIM umber Grip number British pendulum number Stradograph number Stradograph number SCRIM number Grip Number British pendulum number Komatsu skid number Table 5 Coefficients statistically estimated for Eq. (7) for texture measured Laser Profilometer To device values From device values K 1 K 2 K 3 K 4 Standard error British pendulum number SCRIM number Grip number Komatsu skid number Stradograph number Grip number SCRIM number British pendulum number Komatsu skid number Stradograph number SCRIM number Grip number British pendulum number Komatsu skid number Stradograph number Komatsu skid number SCRIM umber Grip number British pendulum number Stradograph number Stradograph number SCRIM number Grip Number British pendulum number Komatsu skid number Fig. 3 Comparison between different equations to estimate SCRIM values from British Pendulum Number Fig. 4 Comparison between equations to estimate SCRIM values from Grip Tester Number

6 122 INDIAN J ENG. MATER. SCI., APRIL 2006 Conclusions Correlation equations obtained from literature do not consider texture. This limits their application to texture range under which they were calibrated. The PIARC procedure considers texture and skid resistance for the estimation of the friction index. This methodology implicitly considers a reference curve for different skid resistance measuring devices. PIARC develop coefficients to correlate different devices to the reference curve, making the harmonization possible. The methodology proposed in this paper considers the reference curve principle developed by PIARC. However, it considers an explicit functional relation between two equipments and the effect of texture on the harmonization. The use of the proposed methodology reduces significantly data requirements for friction harmonization. This is because a high scale harmonization experiment is not needed to determine a reference curve for all devices; instead, measures are needed to correlate each equipment pair. Models developed in this study show that under a specific texture range, non-linear correlations can be neglecting translated in linear relations neglecting the texture effect. For texture measures performed with laser profiler, this range varies from 0.7 to 2.0 mm. For textures measures performed with the sand patch method, this range varies from 1.0 to 2.0 mm. The statistical analysis performed to obtain the coefficients k i shows that the coefficients related to the incidence of macro-texture by itself (k 2 and k 3 ), or associated with skid resistance (k 4 ) are very significant, with t-values considerably higher than 2 in most cases. Then, in order to improve the goodness to fit of the correlation equations, the influence of macro-texture in the estimation of skid resistance cannot be neglected. For values of texture higher than 0.7 mm, a linear relationship between different tribometers is enough to fulfill accuracy. It agrees with the previous experience, as stated in the British standards. The incidence of macro-texture is clearly lower when texture is measured with profilometer. It would allow obtaining better adjustments for the correlation equations when macro-texture is measured with profilometer. Acknowledgements The authors wish to thank Fondo para el Desarrollo de Ciencia y Tecnología (FONDECYT) of Chile, for financial support to carry out the FONDECYT Project Nº References 1 de Solminihac H, Gestión de Infraestructura Vial, Ediciones Universidad Católica de Chile, Chile, Wambold J Antle C Henry, J J & Rado Z, International PIARC Experiment to Compare and Harmonize Texture and Skid Resistance Measurements, PIARC Technical Committee on Surface Characteristics C-1, Descornet G, Proposal for European Standard in relation with the skid resistance of road surfacings, Final Report Belgian Road Research, Vanes, G W H. Skid Resistance measurements on airports runways: correction of self wetting friction measurements devices. Evaluation of the ESDU method, CROW Report 03-10, (2003). 5 de Solminihac H Bustos M Echaveguren T & Caroca A, Revista RIC, 19(2) (2004) FINDLAY IRVINE LTD, Report on Correlation of SCRIM with the Mark 2 GripTester Trial at TRL. Jacobs Babtie, PRO 1961, United Kingdom, Highways Agency, Design Manual for Roads and Bridges, HD 28/94, UK, NORSEMETER FRICTON AS, A primer on modern runway surface friction measurement. NORSEMETER, Norway, Roco V, Fuentes C & Valverde S, Evaluación de la Resistencia al Deslizamiento en Pavimentos Chilenos. Proc VI Congreso Internacional PROVIAL, Chile, Roe P G & Sinhal R, How do you compare? Correlation and calibration of skid resistance and road surface friction measurements devices, paper presented on 1st Surface Friction of Roads and Runways Conference, Christchurch, New Zealand, Transit N Z, Notes to The Specification for Skid Resistance Deficiency Investigation and Treatment Selection, TNZ SP/NT 10, New Zealand, Valenzuela H, Validación del Indice de Fricción Internacional de Pavimentos en Chile. P.E. Degree Thesis. Universidad Central de Chile. Supervisor, Santiago de Chile, Liu Y Fwa T F & Choo Y S, J Test Evaluat, 32(4) (2004) 304.