A study of deterioration in ride quality on Ohio's highways

Transcription

1 The University of Toledo The University of Toledo Digital Repository Theses and Dissertations 2015 A study of deterioration in ride quality on Ohio's highways Vincent Laphang Ng University of Toledo Follow this and additional works at: Recommended Citation Ng, Vincent Laphang, "A study of deterioration in ride quality on Ohio's highways" (2015). Theses and Dissertations This Thesis is brought to you for free and open access by The University of Toledo Digital Repository. It has been accepted for inclusion in Theses and Dissertations by an authorized administrator of The University of Toledo Digital Repository. For more information, please see the repository's About page.

2 ` A Thesis entitled A Study of Deterioration in Ride Quality on Ohio s Highways by Vincent Laphang Ng Submitted to the Graduate Faculty as partial fulfillment of the requirements for the Master of Science Degree in Civil Engineering Dr. Eddie Y. Chou, P.E., Committee Chair Dr. Richard H. Becker, Committee Member Dr. Liangbo Hu, Committee Member Dr. Patricia R. Komuniecki, Dean College of Graduate Studies The University of Toledo May 2015

3 Copyright 2015, Vincent Laphang Ng This document is copyrighted material. Under copyright law, no parts of this document may be reproduced without the expressed permission of the author.

4 An Abstract of A Study of Deterioration in Ride Quality on Ohio s Highways by Vincent Laphang Ng Submitted to the Graduate Faculty as partial fulfillment of the requirements for the Master of Science Degree in Civil Engineering The University of Toledo May 2015 As pavement condition raises more and more concerns in the highway system across the United States, a new challenge emerges in developing reliable pavement deterioration prediction models that are easily applicable by highway pavement management system in state departments of transportation and other agencies. Transportation agencies typically employ a process to evaluate pavement performance on a regular basis and identifying sections with a need for maintenance or rehabilitation. Some states in the U.S. utilize an index based on ride quality alone, while others utilize a pavement rating system that is based solely on visible surface distresses in order to regularly perform evaluation of pavements. This thesis looks at the differences among the combinations of pavement types and priority systems and what effects they have on pavement roughness within the Ohio state highway system when compared with annual snowfall, quantified by the International Roughness Index (IRI). Simple correlation studies between one of Ohio s pavement condition indices, the Pavement Condition Rating (PCR), and RIRI were conducted in attempt to find a meaningful relationship. When little to no correlations were found, this thesis then iii

5 separated the highways based on pavement type, priority system, and annual snowfall region to develop an annual average IRI increase trend for each combination. Statistical testing were performed to ensure results were generated with a 90% confidence level. Slope regression and frequency prediction methods were used in three cases of annual snowfall to analyze the snowfall- RIRI relationship: one was split into four snowfall regions, and the other two were by different amount of snowfall thresholds 30 and 40 inches per year. Results from both methods confirmed that there exists a direct, positive correlation between the amount of annual snowfall and the increase of International Roughness Index per year in nearly all combinations of pavement-priority highways in Ohio. iv

6 This thesis is dedicated to my family. A special gratitude goes out to my loving parents, Amy and Chi Ng, for their unconditional love and constants words of encouragement during the challenges of graduate school and in life. Special thanks also go out to my brother, Edwin Ng, Pharm.D., who has also been there for me through thick and thin. Without you all, I would not be who I am today. I also dedicate this thesis to all my friends, University of Toledo faculties, and staff members who have guided and supported me throughout my undergraduate and graduate years.

7 Acknowledgements I would like to acknowledge my adviser and committee chair, Dr. Eddie Y. Chou, P.E., for providing financial support for me to pursue my Master s Degree. I appreciate his valuable insight and mentorship on my thesis. I am also grateful to my master s thesis advisory committee, Dr. Richard H. Becker and Dr. Liangbo Hu, for their valuable time and helpful suggestions in allowing me to defend this thesis. Acknowledgement also goes to my lab mates, Dr. Shuo Wang, for his time and technical expertise. Special thanks go to the Department of Civil Engineering of the University of Toledo and the City of Toledo Division of Engineering Services for providing graduate assistantship during the course of my graduate study. v

8 Table of Contents Abstract... iii Acknowledgements...v Table of Contents... vi List of Tables... ix List of Figures... xii List of Abbreviations... xiv Chapter Introduction Problem Statement Background Research Objective...5 Chapter Literature Review Pavement Condition and Roughness Indices Present Serviceability Rating (PSR) Pavement Serviceability Index (PSI) Pavement Condition Rating (PCR) International Roughness Index (IRI)...14 vi

9 2.2 Indices Correlations RQI-IRI Correlation PSR-IRI Correlation Pavement Performance Modeling Factors Affecting IRI...26 Chapter Methodology Data Source Ohio PCR- RIRI Correlation RIRI Deterioration by Slope Regression RIRI Deterioration by Frequency...40 Chapter Results and Discussion Results of Ohio PCR- RIRI Correlations Snowfall and Pavement-Priority Deterioration Comparisons Summary of Findings...58 Chapter Conclusions and Recommendations Summary and Conclusions Recommendations for Future Research...62 References...63 Appendix A...69 Appendix B...82 vii

10 Appendix C...84 Appendix D...93 Appendix E...95 viii

11 List of Tables 1.1 FHWA Pavement Condition Criteria FHWA Guidelines for Collecting PSR Data MnDOT Pavement Condition Indices MnDOT Ride Quality Index IRI and RUT Regression Models Ohio State Highway System Mileages Ohio Snowfall Regions inch Snowfall Threshold on Ohio Districts inch Snowfall Threshold on Ohio Districts Summary of PCR- RIRI Correlations Summary of Weighted RIRI Trend on Composite-Priority Regions Statistical Differences among Snowfall Regions on Composite-Priority Summary of Weighted RIRI Trend on Flexible-General Regions Statistical Differences among Snowfall Regions on Flexible-General Statistics of RIRI Trends for Snowfall < 30 in/yr Statistics of RIRI Trends for Snowfall 30 in/yr Statistical Differences of RIRI Trends at Snowfall Threshold of 30 in/yr Statistics of RIRI Trends for Snowfall < 40 in/yr ix

12 4.10 Statistics of RIRI Trends for Snowfall 40 in/yr Statistical Differences of RIRI Trends at Snowfall Threshold of 40 in/yr RIRI Trends by Method on Composite-Priority Pavements RIRI Trends by Method on Flexible-General Pavements Extreme Snowfall RIRI Trends on Composite-Priority Pavements Extreme Snowfall RIRI Trends on General-Flexible Pavements Summary of Snowfall Thresholds on Pavement Roughness Summary of Snowfall Regions on Pavement Roughness Method of RIRI Trends Comparisons...60 A.1 Flexible Pavement Condition Rating Form...69 A.2 Flexible Pavement Condition Rating Form with Deduct Values...70 A.3 Asphalt Surface Local Pavement Condition Rating Form Descriptions...71 A.4 Asphalt Surface Local Pavement Condition Rating Form with Deduct Values...72 A.5 Composite Pavement Condition Rating Form...73 A.6 Composite Pavement Condition Rating Form with Deduct Values...74 A.7 Jointed Concrete Pavement Condition Rating Form...75 A.8 Jointed Concrete Pavement Condition Rating Form with Deduct Values...76 A.9 Continuously Reinforced Concrete Pavement Condition Rating Form...77 A.10 Continuously Reinforced Concrete Pavement Condition Rating Form with Deduct Values...78 A.11 Brick Paver Condition Rating Form Descriptions...79 A.12 Brick Paver Condition Rating Form with Deduct Values...80 B.1 Bituminous Pavement Surface Rating Weighting Factors...81 x

13 B.2 Concrete Pavement Surface Rating Weighting Factors...82 B.3 Continuously Reinforced Concrete Pavement Surface Rating Weighting Factors...82 D.1 Sample PCR- RIRI Correlation Data...92 E.1 Sample RIRI and Pavement Age Data...94 xi

14 List of Figures 1-1 Aspects of IRI Calculation IRI Interpretation Scale Concept of Pavement Performance using Present Serviceability Index ODOT Pavement Condition Rating Scale Quarter Car Simulation IRI to RQI Conversion based on the 1997 Rating Panel TWD to SR Conversion Factors Affecting Pavement Performance Mechanistic-Empirical Model Design Process Ohio Highway Priority Systems Ohio Highway Pavement Types Ohio Annual Snowfall Ohio Annual snowfall with District Numbers year PCR- RIRI Correlation year PCR- RIRI Correlation year PCR- RIRI Correlation year PCR- RIRI Correlation year PCR- RIRI Correlation...46 xii

15 4-6 Frequency Plot of Composite-Priority Highways at 30-inch Snow Threshold Frequency Plot of General-Flexible Highways at 30-inch Snow Threshold Extreme Snowfall RIRI Frequency Plot on Composite-Priority Highways Extreme Snowfall RIRI Frequency Plot on General-Flexible Highways...57 A-1 ODOT Pavement Condition Rating Scale...68 C-1 ODOT Composite Pavement General System Highways...83 C-2 ODOT Composite Pavement Priority System Highways...84 C-3 ODOT Composite Pavement Urban System Highways...85 C-4 ODOT Flexible Pavement General System Highways...86 C-5 ODOT Flexible Pavement Priority System Highways...87 C-6 ODOT Flexible Pavement Urban System Highways...88 C-7 ODOT Jointed Concrete Pavement General System Highways...89 C-8 ODOT Jointed Concrete Pavement Priority System Highways...90 C-9 ODOT Jointed Concrete Pavement Urban System Highways...91 xiii

16 List of Abbreviations AASHO...American Association of State Highway Officials (prior to 1973) AASHTO...American Association of State Highway and Transportation Officials (since 1973) ASTM....American Society for Testing and Materials (prior to 2002) (named as ASTM International since) FHWA.....Federal Highway Administration GIS Geographic Information System HPMS....Highway Pavement Management System HRI....Half-car Roughness Index INDOT...Indiana Department of Transportation IRI..International Roughness Index LTPP...Long-Term Pavement Performance MEPDG...Mechanistic-Empirical Pavement Design Guide MRI...Mean Roughness Index NCDC...National Climatic Data Center NCHRP.. National Cooperative Highway Research Program NHS...National Highway System NLFID...Network Linear Feature Identifier NOAA....National Oceanic and Atmospheric Administration ODOT....Ohio Department of Transportation PCI...Pavement Condition Index PI Profile Index PMS...Pavement Management System PMIS...Pavement Management Information System PQI.....Pavement Quality Index PSI....Present Serviceability Index PSR...Present Serviceability Rating RIRI...Right-wheel International Roughness Index RN..Ride Number RQI....Ride Quality Index SAS... Statistical Analysis System SR......Surface Rating TWD...Total Weighted Distress xiv

17 Chapter 1 Introduction 1.1 Problem Statement With roadway pavements constantly deteriorating, responsible agencies must stay ahead in terms of repairing and rehabilitating. However, with over 19,225 centerline miles and nearly 49,400 lane-miles spanned across the state of Ohio s highway system, keeping track of every route may be a difficult task, which may lead to some routes being neglected, thus their pavement condition becomes worse and worse (ODOT, 2014). Using a predictive model on pavement performance based on past data along with Geographic Information Systems (GIS) as a visual aid can help engineers and officials better visualize the Ohio Department of Transportation s (ODOT) vast highway system and save significant amount of money and time for pavement infrastructure management agencies through better planning, maintenance, and rehabilitation activities. 1

18 1.2 Background All road pavements continuously deteriorate under the combination of environmental effects and traffic loading. The ability of the road to satisfy the demands of the environment and traffic over its design life is known as performance according to the American Association of State Highway and Transportation Officials (AASHTO). The American Society of Testing and Materials (ASTM) defines roughness as the deviation of a surface from a true planar surface with characteristic dimensions that affect vehicle dynamics and ride quality (Prozzi, 2001). Roadway roughness, or smoothness, measurements are performed to monitor the condition of a road network or to evaluate the ride quality of newly constructed or rehabilitated pavements in a pavement management system (PMS). Profile data are gathered to analyze the condition of specific sites and to determine applicable countermeasures. Many roughness indices are acquired from profile data along with visual inspection and/or correlated with road users perception of ride quality to determine the level of roughness. Some include Present Serviceability Index (PSI), Present Serviceability Rating, Profile Index (PI), Ride Quality Index (RQI), Ride Number (RN), Half-car Roughness Index (HRI), and Mean Roughness Index (MRI). Because these indices are measured differently and some may be user-subjective, therefore it makes initial and subsequent roughness comparisons difficult. In order to unify all roughness indices, the U.S. and many other countries around the globe are currently using the International Roughness Index (IRI), which was initiated by 2

19 the World Bank in Today, ASTM Standard E defines the standard procedure for computing the IRI from longitudinal profile measurements. The quarter-car is moved along the longitudinal profile at a simulation speed of 80km/hr. (50mph), and the suspension deflection is calculated using the measured profile displacement and standard car structure parameters. The simulated suspension motion is accumulated and is then divided by the distance traveled to give an index in terms of slope, such as millimeters per meter or inches per mile (Sayers & Karamihas, 1998). The following figures provide a visual representation of how IRI is generated and comparisons between types of pavements against speed of normal use. Figure 1-1 Aspects of IRI Calculation (Sayers & Karamihas, 1998) 3

20 Figure 1-2 IRI Interpretation Scale (Sayers & Karamihas, 1998) Since 1990, the Federal Highway Administration (FHWA) requires states to collect and report the IRI values for roadways within the National Highway System (NHS) which fall within their respective jurisdictions (Wang, 2006). FHWA has established criteria for defining acceptable ride quality. Tables 1.1 (a) and (b) show the previous and current ratings employed by FHWA for NHS routes. Under the previous system, pavements were classified as very good if IRI were less than 60in/mile (0.95mm/m) and poor if IRI were greater than 170in/mile (2.68mm/m) for interstates or greater than 220in/mile (3.47mm/m) for other routes. Under the current system, there are two categories: good 4

21 corresponding to IRI is than 95in/mile (1.5mm/m) and acceptable corresponding to IRI not greater than or equal to 170in/mile (2.68mm/m). Table 1.1 FHWA Pavement Condition Criteria (Reza et al., 2005) (a) Previous Criteria Interstate IRI Rating Previous Condition Term Categories (inches/mile) Other IRI Rating (inches/mile) Very Good < 60 < 60 Good 60 to to 94 Fair 95 to to 170 Mediocre 120 to to 220 Poor > 170 > 220 (b) Current Criteria New Condition Term Categories IRI Rating (inches/mile) Good < 95 Acceptable 170 Since the publication of guidelines for obtaining IRI in 1986, it has become the most widely-used and reproducible indicator of road roughness (Sayers et al., 1986). IRI is required for all data submitted to the Federal Highway Administration (FHWA) highway performance monitoring system (HPMS) database and is stored in the long-term pavement performance (LTPP) database (FHWA, 1990). 1.3 Research Objective The main objective of this research is to investigate the deterioration of ride quality as measured by the International Roughness Index (IRI) on Ohio s state highway system. Factors such as geographical location, snowfall amount, pavement type, priority system, 5

22 etc. are investigated, in order to establish a predictive model for IRI deterioration trend. This research focuses on a methodology to develop a model that predicts the deterioration of IRI as a function of annual geographic snowfall, Ohio Department of Transportation s (ODOT) state highway priority systems, and pavement types. The study area considered the entire state of Ohio, and the following factors were taken into account in this analysis: district, county, route, pavement age, begin-log (Blog), end-log (Elog), right-wheel IRI (RIRI), pavement type, and priority systems. 6

23 Chapter 2 Literature Review Pavement performance is defined as the ability of a pavement to satisfactorily serve traffic over time (AASHTO, 2003). Specifically, pavement roughness is generally defined as an expression of irregularities in the pavement surface that adversely affect the ride quality of a vehicle (Pavement Interactive, 2007). Roughness, also referred to as smoothness, is a crucial pavement property since it affects not only the ride quality but also fuel consumption, vehicle delay costs, and maintenance cost (UMTRI, 2002). In order to establish pavement performance and distress models, it is imperative to know the current approaches used for pavement conditions evaluation and forecasting. 2.1 Pavement Condition and Roughness Indices Oftentimes, pavement distress information is converted into a condition index. The condition index incorporates information across all distress types and severities into one single number. This number can then be used at the network level to represent the condition state, to determine when treatments are needed, to rank or prioritize, and to forecast pavement condition. The condition index may either represent one single distress 7

24 such as longitudinal cracking or a combination of pavement distresses, which is then referred to as a composite index (Alkire, 2009). Nowadays, pavement condition is commonly quantified by either the Present Serviceability Index (PSI) or Pavement Condition Rating (PCR), while pavement roughness is quantified by the International Roughness Index (IRI) Present Serviceability Rating (PSR) One of the earliest efforts to quantify pavement condition was the development of the Present Serviceability Rating (PSR) by the American Association of State Highway Officials (AASHO) Road Test, which revolved the concept of serviceability. Serviceability is defined as the ability of a pavement to serve traffic (ODOT, 2014). PSR was developed to quantify serviceability. It is defined as the judgment of an observer as to the current ability of a pavement to serve the traffic it is meant to serve (Highway Research Board, 1972). PSR is a discrete numerical rating of pavement ride based on a 0- to-5 scale, with zero being impassible and five being perfect. This method required a panel of raters to ride in an automobile over the observed pavement. Today, the Federal Highway Administration (FHWA) still requires states to submit PSR data for nationwide road health monitoring. The FHWA guidelines for collecting the PSR data are shown in Table

25 Table 2.1 FHWA Guidelines for Collecting PSR Data (Lefler et al., 2010) PSR Description Only new (or nearly new) superior pavements are likely to be smooth enough and distress free (sufficiently free of cracks and patches) to qualify for this category. Most pavements constructed or resurfaced during the data year would normally be rated in this category. Pavements in this category, although not quite as smooth as those described above, give a first class ride and exhibit few, if any, visible signs of surface deterioration. Flexible pavements may be beginning to show evidence of rutting and fine random cracks. Rigid pavements may be beginning to show evidence of slight surface deterioration, such as minor cracks and spalling. The riding qualities of pavements in this category are noticeably inferior to those of new pavements, and may be barely tolerable for high-speed traffic. Surface defects of flexible pavements may include rutting, map cracking, and extensive patching. Rigid pavements in this group may have a few joint failures, faulting and/or cracking, and some pumping. Pavements in this category have deteriorated to such an extent that they affect the speed of free-flow traffic. Flexible pavement may have large potholes and deep cracks. Distress includes raveling, cracking, rutting and occurs over 50% of the surface. Rigid pavement distress includes joint spalling, patching, cracking, scaling, and may include pumping and faulting. Pavements in this category are in an extremely deteriorated condition. The facility is passable only at reduced speeds, and with considerable ride discomfort. Large potholes and deep cracks exist. Distress occurs over 75% or more of the surface. To avoid the impracticality of having raters ride over every pavement, a relationship was developed between the mean PSR assigned by the panel and some objective measurements such as roughness, rutting, and cracking. The new index, which was based 9

26 on the values of pavement roughness, rutting, cracking, and patching, was called the Present Serviceability Index (PSI) (Pavement Interactive, 2006) Present Serviceability Index (PSI) To transition from a panel-required PSR measure to panel-less Pavement Serviceability Index (PSI) measure, a panel of raters evaluated various roads across the states of Illinois, Indiana and Minnesota for PSR. This information was then correlated to different pavement measurements, such as profile and distresses, to develop the following PSI equations (Alkire, 2009): PSI = 5.03 log(1 + SV) 1.38(RD) C + P (1) Where: PSI = Present Serviceability Index, which is a statistical estimate of the mean of the Present Serviceability Rating given by the panel SV RD = slope variance over section from CHLOE profilometer = mean rut depth (in.) C = cracking, flexible (ft./1,000ft. 2 ) P = patching (ft. 2 /1,000ft. 2 ) In addition, the raters provided an opinion as to whether a specific pavement assessed for PSR was acceptable or unacceptable as a highway. Therefore, unlike PSR, PSI offers more than just a 5-point rating system; it adds a terminal serviceability design input. It 10

27 was found that the raters deemed a PSR of 3.0 was acceptable and a PSR of 2.5 was not (Pavement Interactive, 2006). Figure 2-1 illustrates graphically the concept of PSI. Figure 2-1 Concept of Pavement Performance using Present Serviceability Index (Hveen & Carmany, 1948) Pavement Condition Rating (PCR) Pavement distress, categorized by pavement type and distress type, is evaluated in terms of severity and extent. The detailed procedure for assessing Pavement Condition Rating (PCR) of a pavement in the state of Ohio is outlined in the Pavement Condition Rating System manual (2006), prepared by the Ohio Department of Transportation (ODOT). A PCR is based upon the summation of deduct points for each type of observable distress. Deduct values are a function of distress type, severity, and extent. The deduction for each distress type is calculated by multiplying the weights of distress, severity, and extent. The mathematical expression for PCR is as follows: 11

28 PCR = 100 n 1 Deduct i (2) Where: n = number of observable distresses Deduct i = (weight of distress) x (weight of severity) x (weight of extent) for distress i The distress types are accordingly defined for six types of pavement: flexible, composite, jointed concrete, continuously reinforced concrete, local, and brick pavers. For flexible and local pavements, visual inspections of distress include raveling, bleeding, patching, debonding, crack sealing deficiency, rutting, settlements, potholes, wheel track cracking, block and transverse cracking, longitudinal cracking, edge cracking, and thermal cracking. For both concretes, they include surface deterioration, longitudinal joint spalling, patching, pumping, faulting, settlements, transverse joint spalling, transverse cracking, pressure damage, transverse cracking, longitudinal cracking, and corner breaks. For brick pavers, distress includes brick deterioration, discoloration, patching, pumping, rutting, corrugations, joint erosion, and brick settlement. For composite pavements, they include raveling, bleeding, patching, surface disintegration (debonding), rutting, pumping, shattered slab, settlements, transverse cracks, joint reflection cracks, intermediate transverse cracks, longitudinal cracking, pressure damage/upheaval, crack seal deficiency, corner breaks, and punchouts (ODOT, 2006). Three levels of severity (low, medium, and high) and three levels of extents (occasional, frequent, and extensive) are defined per each distress. Figure 2-2 depicts the PCR scale 12

29 and how ODOT associates the various ranges of PCR values. The weights of each pavement type are specified in the manual in Appendix A. A perfect PCR of 100 signifies an ideal pavement without any observable distress, and a PCR of 0 signifies a pavement with all distress at their high levels in severity with extensive levels in extent. Figure 2-2 ODOT Pavement Condition Rating Scale (ODOT, 2006) The major reasons why ODOT uses PCR are: (1) it improves the intercommunication between state highway agency and the highway engineers. (2) PCR enables ODOT to create a standardized critical threshold level below which the pavement is classified as unacceptable and major reconstruction or rehabilitation are required. It is also a possible for ODOT to create other threshold levels whereby one level expresses the need of routine maintenance, one for minor repairs, another one for major rehabilitation, etc. (3) 13

30 PCR ratings allow ODOT to rank and prioritize roads and highways for their maintenance and/or rehabilitation activities. (4) PCR collected over a period of time can allow ODOT to predict the rate of deterioration on different sections of pavement across the network. (5) The distress indices allow pavement engineers to reexamine previous design methods and analyze the effects of various design attributes on pavement distress, which can help improving future designs (ODOT, 2014) International Roughness Index (IRI) The International Roughness Index (IRI) is a statistic index that summarizes the surface deviations for just one wheel track. This mathematical simulation uses the quarter car system to generate an imaginary profile. As shown in Figure 2-3, the quarter car is made up of two parts: a sprung mass representing the vehicle body where the user sits and an unsprung mass, representing the set of wheel and suspension. The sprung mass is connected to the unsprung mass by the suspension, which is simulated by a damper and a spring. The sprung mass is in contact with the real pavement surface by another spring (Arellano et al., 2006). Figure 2-3 Quarter Car Simulation (Shahin, 1994) 14

31 During simulation, the quarter car system rolls over the longitudinal profile of the pavement at a constant speed of 80km/hr. (50mph). The roughness over this pavement induces dynamic excitation to the quarter car system, generating different vertical speeds or accelerations (z s and z u ) in the sprung and unsprung masses. As a result, a relative movement is produced between the chasses and the axle of the imaginary vehicle. The IRI value for a given section length is computed according to the following equation (Arellano et al., 2006): IRI = 1 L x/v 0 z ṡ z u dt (3) Where: IRI = International Roughness Index (mm/m or m/km) L x V x / V dt z s z u = length of section (m) = longitudinal distance (m) = speed of the quarter-car model (m/s) = time it takes the model to run a certain distance x = time increment = vertical speed of the sprung mass = vertical speed of the unsprung mass In 2002, the FHWA defined acceptable roads within the national highway system (NHS) as having an IRI of 170 inches/mile or less. Furthermore, roads with an IRI of 95 inches/mile or less were considered as good quality (FHWA, 2015). Today, the specifications for obtaining IRI are set by ASTM International (2008), specifically 15

32 ASTM Standard E The IRI for the right wheel track (RIRI) is the measurement of road surface roughness specified by the Federal Highway Administration (FHWA) as the input to their highway performance monitoring system (HPMS). IRI is widely used today and is considered as the predominant index for quantifying pavement roughness because it is portable in a sense that it can be obtained with a variety of measurements. In addition, IRI is stable with time because it is based on the concept of a true longitudinal profile rather than the physical properties of a particular type of instrument. 2.2 Indices Correlations In order to unify the many pavement condition and roughness indices, researchers over the years have been developing relationships between the combinations of the Present Serviceability Rating (PSR), Pavement Condition Rating (PCR), and the International Roughness Index (IRI) over a range of existing conditions on highways in both urban and rural areas across the country RQI-IRI Correlation The Minnesota Department of Transportation (MnDOT) currently uses three indices to evaluate pavement conditions. One index represents pavement roughness; one represents pavement distress; one represents the overall pavement quality. Shown in Table 2.2, these indices are used to quantify the present pavement condition and to predict future 16

33 condition. For each index, a higher numerical value represents better pavement condition (MnDOT, 2006). Table 2.2 MnDOT Pavement Condition Indices (MnDOT, 2006) Index Name Pavement Attribute Measured by Index Rating Scale Ride Quality Index (RQI) Pavement Roughness 0.0~5.0 Surface Rating (SR) Pavement Distress 0.0~4.0 Pavement Quality Index (PQI) Overall Pavement Quality 0.0~4.5 The relationship among the three indices is as follow: PQI = RQI SR (4) Many states use the International Roughness Index (IRI) as their only measure of roughness. However, Minnesota converts IRI into RQI so that drivers opinions are considered. This step is important since it forms a baseline of what IRI drivers feel is unacceptable. To convert IRI to RQI, a correlation was developed through the use of a rating panel. When last used in 1997, 32 observers rated over 120 quarter-mile long sections, which included all types of pavement. Each panelist assigned a numerical value from zero to five to each segment based on the following scale: 17

34 Table 2.3 MnDOT Ride Quality Index (MnDOT, 2006) Numerical Rating Verbal Rating 4.1~5.0 Very Good 3.1~4.0 Good 2.1~3.0 Fair 1.1~2.0 Poor 0.0~1.0 Very Poor Using regression analysis, the panelists RQI was correlated to the measured IRI. Figure 2-3 shows the separate curves for bituminous and concrete pavements. The following equations were generated for bituminous and concrete pavements: Figure 2-4 IRI to RQI Conversion based on the 1997 Rating Panel (MnDOT, 2006) Bituminous Pavements: RQI = IRI, where IRI is in meters per kilometer (5) 18

35 RQI = IRI, where IRI is in inches per mile (6) Concrete Pavements: RQI = IRI, where IRI is in meters per kilometer (7) RQI = IRI, where IRI is in inches per mile (8) As a result of this correlation, the ride quality can be used to estimate how a panel of observers would rate the pavement. This correlation is valid as long as the public s perception of smooth and rough roads does not change considerably. MnDOT also uses Surface Rating (SR) to qualify pavement distresses, which are visible defects on the pavement surface. Rather than continuously conducting distress surveys, MnDOT only collects and rates the first 500 feet of each mile and section due to time requirement. The percentage of each distress in the 500-foot sample is determined and multiplied by a weighting factor to yield a weighted percentage, listed in Appendix B. The weighting factors are higher for more severe distresses such as alligator cracking. When all weighted percentages are determined, they are summed to give the Total Weighted Distress (TWD). The SR is then calculated from TWD using the following correlation or by using Figure 2-5: SR = e TWD (9) 19

36 Figure 2-5 TWD to SR Conversion (MnDOT, 2006) PSR-IRI Correlation Al-Omari and Darter (1994) found that the database from the National Cooperative Highway Research Program (NCHRP) Project 1-23 have the most comprehensive database along with supplementary data from Indiana. The relationship between PSR and IRI was then analyzed for five additional states obtained from the NCHRP Project The six states included Indiana, Louisiana, Michigan, New Jersey, New Mexico, and Ohio. Through a statistical analysis system (SAS), the authors concluded that there were no significant differences between the models for different states and pavement types (flexible, rigid, and composite). The following nonlinear model that fits the boundary conditions was recommended: 20

37 PSR = 5 e 0.26 IRI, where IRI is in millimeters per meter (10) PSR = 5 e IRI, where IRI is in inches per mile (11) 2.3 Pavement Performance Modeling Pavement surface roughness is widely used as the pavement condition parameter worldwide, because it is relatively inexpensive to collect, its objective nature, and its high correlation with road user costs. It has also been accepted as the most relevant measure of long-term functional behavior for a pavement network. This information could then be translated into better pavement designs, strategic planning, maintenance, and as well as rehabilitation forecasting (Martin, 1996). Roughness modeling aids in the form of lifespan prediction, which in turns affects financial evaluation, remaining life prediction, and overall network condition (Ping & Yunxia, 1998). Age, pavement materials, subgrade composition, traffic and weather are just some of many factors that affect road performance, as shown in Figure 3. These elements could be classified into five categories: environment (moisture, radiation, freeze-thaw cycles, temperature), structure (layer and variations in thickness, layer and subgrade types and properties), construction (timing, methods, as-built quality, variance), maintenance (treatments, timing, methods, quality), and traffic (axle group loads, tire types and pressure, axle spacing, speed repetitions) (Saba et al. 2006). 21

38 Figure 2-6 Factors affecting Pavement Performance (Haas, 2003) One of the most difficult problems that pavement engineers face has been the development of deterioration or performance models. Over the years, many simple and complex models have been developed. Ralph Haas (2003) categorized the many performance prediction models into types which indicate their principles as follows: Empirical, where certain measured or estimated variables such as traffic loads and vertical deflections are related to the loss of serviceability, pavement age, or a measure of deterioration, often through regression analysis. Mechanistic-empirical, where certain calculated responses, such as subgrade or pavement stress and strain, together with other variables such as traffic loads, are related to the loss of serviceability or a measure of deterioration through 22

39 regression analysis, which is previously calibrated (i.e. the coefficients are determined). Subjective, loss of serviceability or a measure of deterioration versus age is correlated, for different combination of variables, through Markovian models, Bayesian models, etc. An example of an empirical pavement performance model was conducted by Gulen et al. for the Indiana Department of Transportation (INDOT), where 1999 s and 2000 data were used to develop regression models for different pavement types for interstate as well as non-interstate roads. The International Roughness Index (IRI), in inches per mile, was used mainly for dependent variables while the age of pavement (AGE) and the current average annual daily traffic (AADT) were served as independent variables in search for the best models (Gulen et al., 2001). For any pavement management system (PMS), the ability to accurately and reliably predict pavement performance based on roughness, rutting, and other distress measurements is essential. The available data plays a crucial role in determining the quality of pavement performance prediction models. With more collected data such as IRI, rutting (RUT longitudinal deformation of the pavement in the wheel tracks, measured in inches Indiana Design Manual 2006), and traffic volumes than previously before, Gulen et al. developed the following improved models to predict IRI for the following types of pavements: 23

40 Table 2.4 IRI and RUT Regression Models (Gulen et al., 2001) Type of Road Surface Prediction Model R 2 IR C&S IRI = * AGE * AADT 0.24 IR FLEX IRI = * AGE * AADT 0.70 IR FLEX RUT = * AGE * ADT 0.10 IR JCP IRI = * AGE * AADT 0.50 IR THIN IRI = * AGE * AADT 0.34 IR THIN RUT = * AGE * AADT 0.66 IR OVERLAY IRI = * AGE * AADT 0.15 Non- IR ASPH IRI = * AGE * AADT 0.30 Non- IR ASPH RUT = * AGE * AADT 0.26 Non- IR JCP IRI = * AGE * AADT 0.27 Non- IR OVERLAY IRI = * AGE * AADT 0.90 Non- IR OVERLAY RUT = * AGE * AADT 0.99 Where: IR = interstate C&S FLEX JCP THIN OVERLAY R 2 = crack and seated pavement = flexible pavement = jointed concrete pavement = 1¼-inch thick asphalt overlay over existing asphalt = asphalt overlay over existing concrete pavement = correlation of determination (i.e. percent of information for the dependent variable that could be obtained from the regression model) 24

41 Another type of pavement performance model is the mechanistic-empirical approach. Referred to as the Mechanistic-Empirical Pavement Design Guide (MEPDG), its objective is to determine the physical causes of distress in pavement structure and correct them with observed pavement performance. This allows engineers to design for acceptable levels of distress over the predetermined design period. These two elements define this approach to pavement design, where mechanistic focuses on physical causes while observed performance determines the empirical part. Figure 2-7 Mechanistic-Empirical Model Design Process (FHWA, 2008) Rather than being a design tool, MEPDG serves as a pavement analysis tool. An initial design is first selected with material properties (e.g. IRI, rutting, longitudinal and transverse cracking, joint faulting), traffic loadings, and climatic conditions determined. The response of that initial design is then generated and related to pavement damage and 25

42 distress. If the projected distress is within the allowable level, then the design is considered as acceptable; otherwise, iterations with different combinations of its dependent variable need to be modified and performed until the projected distress becomes satisfactory (Pavement Interactive, 2012). This process is illustrated in the above figure. The primary advantage of this new design guide is the improved reliability of the resultant designs and the capability to handle any and all combinations of materials, traffic, and climatic conditions. This drastically improves over the commonly used 1986/1993 AASHTO Design Guide for Pavement Structures, which was based on short test period, limited materials, single climatic zone, and low traffic volumes (FHWA, 2008). Since the mechanistic-empirical design is an iterative process, therefore each of the performance indicators needs to be refined until an optimal pavement design is achieved. In using the MEPDG to design pavements, making these comparisons would give engineers a better idea of which factors have the most impact in a given situation. 2.4 Factors Affecting IRI Precipitation, freeze-thaw cycles, and climate cycles are the leading causes of some main distresses such as longitudinal and transverse cracking. Research on pavement performance has shown that with the exception of longitudinal and wheel track cracking formation, roughness of the road increases with additional precipitation (Li et al., 2011). Pavements experience thermal cracking due to compressive and tensile stress when temperature fluctuates. Smith et al. (2008) presented a correlation between temperature 26

43 and pavement deterioration, where a rise in temperature leads to rutting and cracking in the pavement. In cold regions, water penetrates into the subgrades of the pavement and freezes during the winter. Thaw of these blocks of ice during the spring results in deformation of pavement and forms fatigue/wheel track cracking (Jackson & Puccinelli, 2006). In addition, snow storms and floods can exacerbate road conditions by generating shear stress that leads to failure and cracking, widening the existing cracks, and weakening the subgrade, primarily due to the drastic increase in moisture content it causes in the pavement layers. Examples in recent years that demonstrate the drastic effect severe weather and climatic events could have on pavements include: the devastation of New Orleans caused mainly by the breach of a levee during Hurricane Katrina, the aftermath of Hurricane Sandy for the states of New York and New Jersey, and a 16% immediate drop in road conditions measured by the Pavement Condition Index (PCI) in Denver, Colorado due to severe snow storms in 2006 (Gaspard et al., 2006 and Kennedy & Hager, 2008). Shamsabadi et al., (2014) collected snow storm and flood data from the long-term pavement performance (LTPP) program and the National Oceanic and Atmospheric Administration (NOAA) on the states of Florida, Illinois, New Jersey, and Ohio as they had the most extensive data on LTPP and are more vulnerable to frequent snow storms. Through data fusion algorithms and pattern recognition, the authors came up with the following stepwise regression models, which yielded more than 90% correlation: 27

44 For snow storms, % IRI = NIRI + 1.7NDepth 1.74NDuration ESAL Duration (12) For floods, % IRI = NIRI NDepth 2.10NDuration Depth IRI (13) Where: % IRI = percent increase in IRI due to the snow storm NIRI NDepth NDuration ESAL = normalized IRI of the section before the snow storm = normalized depth of the snowfall = normalized duration of the snow storm = equivalent single-axle load (derived from traffic) Nasimifar et al., (2011) obtained climatic data from meteorological organizations for various regions of Iran in hopes to assess the environmental effects on fatigue cracking. The authors concluded that the dynamic modulus of asphalt with any structural specifications decreases in pavements and fatigue damages increase when the mean annual air temperature rises. It was also found that decreasing the subgrade modulus due to increasing moisture causes tensile stress on the bottom of the asphalt layer to rise, thus increasing alligator/wheel track cracking. The authors recommended the use of asphalt mixture with a low dynamic modulus since it helps reduce the negative effects of alligator cracking. The authors also suggested that having better drainage systems would reduce the amount of alligator cracking as well. 28

45 Chapter 3 Methodology 3.1 Data Source The data used to demonstrate the methodology were collected from the Ohio Department of Transportation, who developed a comprehensive Pavement Management Information System (PMIS), which maintains an extensive pavement condition and project history database. Since 1985, pavement condition data have been collected yearly. In addition, pavement project data are also available. Based on these data, pavement performance for each pavement section can be obtained along with its location, year constructed, and construction history. ODOT divides it highway system into priority, general, and urban systems. Figure 3-1 shows the complete Ohio highway priority system and its districts. Priority system consists primarily of interstate and other rural, multi-lane divided highways. General system consists primarily of rural, two-lane U.S. and state routes, and urban system consists primarily of U.S. and state routes inside municipalities with population greater than 5,000 (FHWA, 2012). 29

46 Figure 3-1 Ohio Highway Priority Systems Ohio s highways are primarily made up of three pavement types: composite, flexible, and jointed concrete. Composite pavements are often the results of rehabilitating an original concrete pavement by adding an asphalt surface layer, resulting in a rigid base with an asphalt surface. Flexible pavements are essentially asphalt pavements designed to flex to accommodate traffic loads. Their designs are based on relatively few input parameters, such as serviceability, traffic loading, subgrade stiffness reliability, and structural 30

47 coefficient. Rigid pavements such as jointed concrete are often made out of Portland cement concrete. They are called rigid due to their high modulus of elasticity and are designed based on the modulus of rupture, load transfer coefficient, composite modulus of subgrade reaction, and effective modulus of subgrade reaction (ODOT, 2014). It is known in Ohio that the majority of pavements are flexible pavements and fall under the general system (ODOT, 2004). Figure 1-4 shows the complete Ohio highway pavement system and its districts. Figure3-2 Ohio Highway Pavement Types 31

48 The Ohio Department of Transportation (ODOT) has developed a comprehensive Pavement Management Information System (PMIS) to provide a collection of tools in order to report average performance curves, average conditions at rehabilitation, IRI change list, etc. on all Interstate Routes (IR), U.S. Routes (US), and State Routes (SR) across the state of Ohio. Its data include district, network linear feature identifier (NLFID), county, route number, station, year, begin log (Blog), end log (Elog), half-car simulation (HCS), left International Roughness Index (LIRI), right International Roughness Index (RIRI), ride number (RN), jurisdiction, mile class, surface type, surface width, sum roadway width, National Highway System (NHS), route type, divided or divided, access control, urban area code, functional class, truck ADT, total ADT, ESAL*1000, mile post, rater, pavement type, project number, number of lanes, Pavement Condition Rating (PCR) rate date, pave type, priority system, distress values, PCR value, Cracking Deduct (CRD), Structural Deduct (STRD), Total Deduct (TDC), route system, base, Pavement Quality Index (PQI), and IRI average. This study primarily focused on the following variables: 1. Districts since geographical location relates to the amount of snowfall annually 2. Route Number since RIRI and PCR comparisons need to be made on the same route for consistency 3. Station (up or down) since RIRI differs depending on the direction the quarter car is moved along the pavement 4. Year (of which data is collected) since RIRI increases and PCR decreases naturally with pavement age 32

49 5. Begin Log and End Logs (Blog and Elog) since RIRI and PCR comparisons need to be made on the same route sections over the years for consistency 6. Right IRI (RIRI) since it is the measurement of road surface roughness specified by the Federal Highway Administration (FHWA) 7. Pavement Type 8. Priority System 9. PCR since it is the primary measurement of road surface condition in the state of Ohio The following figure from ODOT Snow & Ice Practices (2011) provides a visual representation regarding the annual snowfall for the state of Ohio. Although the National Climatic Data Center (NCDC) of the National Oceanic and Atmospheric Administration (NOAA) contains daily snowfall data at the county level across the nation dating back to 2005, it is important to point out that none of its data were used due to the overwhelming amount of missing data. 33

50 Figure 3-3 Ohio Annual Snowfall (ODOT, 2011) 3.2 Ohio PCR- RIRI Correlation This study first correlated the change in Pavement Condition Ratings ( PCR) and the change in right-wheel International Roughness Indices ( RIRI) on ODOT s general 34

51 system with flexible pavements obtained from the Ohio Department of Transportation Pavement Management Information System (ODOTPMIS). This combination of generalflexible was used since it makes up the majority of Ohio s highways s data were used as a baseline to compare against 2012, 2011, 2010, 2008, and 2003 s data to generate a 1-year, 2-year, 3-year, 5-year, and 10-year correlation graphs, respectively, in order to ensure a relationship exists in both short- and relatively long-term as the pavement ages. In order to produce reliable results, the PCR and RIRI of each section (Blog and Elog) of each route through a particular county in a particular district had to match from year to year in all correlations. These data can be found in Appendix D. The change in PCR and RIRI were calculated by the following formulas and were then plotted. A coefficient of correlation for each plot was computed as well. A summary of the correlation results is listed in Chapter 4.1 on page 42. PCR = PCR 2013 PCR i (14) RIRI = RIRI 2013 RIRI i (15) Where: PCR = the absolute difference in PCR between 2013 and year i PCR2013 = PCR in 2013 PCRi RIRI = PCR in year i = the absolute difference in RIRI between 2013 and year i 35

52 RIRI2013 = RIRI in 2013 RIRIi = RIRI in year i 3.3 RIRI Deterioration by Slope Regression The data of all districts, counties, routes, station, Blog, Elog, year, as well as RIRI were extracted from ODOTPMIS for the following six combinations of pavement type and priority system for years 2000 to 2013: composite general, composite priority, flexible general, flexible priority, jointed concrete general, and jointed concrete priority. Due to the rarity of jointed concrete pavements as well as urban system in the Ohio highway network, as illustrated by their segment length in Table 3.1, both categories were disregarded in this snowfall comparison, leaving with four pavement-priority combinations: composite-priority, composite-general, flexible-priority, and flexiblegeneral. These extractions of data were done so that they included all major and minor rehabilitation treatments beginning and ending activities and neglected any preventive maintenance during those years. To remove any outliers, the lowest RIRI for each section of route was set as the initial RIRI (i.e. RIRI0), and any decrease in RIRI after RIRI0 were deleted. A sample of the data used can be found in Appendix E. 36

53 Table 3.1 Ohio State Highway System Mileage Mileages (ODOTPMIS) Pavement Type Flexible Composite Jointed Concrete Priority System # of Unique Highway Segments Segment Length Equivalent (miles) Representation in Ohio General 6,076 10, % Priority 1, % Urban 1, % General 3,124 3, % Priority 3,441 1, % Urban 2,750 1, % General % Priority % Urban % The SLOPE function in Microsoft Excel, which returns the slope of the linear regression line through the given data points, was used to calculate the regression line of RIRI against age of pavement after major and minor treatments for all sections of all routes. Since the section lengths (i.e. the difference between Blog and Elog in miles) played an important factor, they were treated as the weights for the slopes. Therefore, weighted slopes for each section of pavement were calculated using the equation below: Weighted Slope = Slope (Elog Blog) (16) The weighted average slope (17) for each pavement-priority combination was then computed so that it can be compared against other combinations. The weighted standard deviations were also computed using equation (18). Weighted Average Slope = 37 (weighted slopes) (section lengths) (17)

54 N i=1[w i (x i x ) 2 ] S d = ( M 1 M ) N i=1 (W i) (18) Where: Sd = weighted standard deviation N = number of total observations Wi = weight of i (i.e. section length of i) xi X M = weighted slope of the i th observation = weighted average slope = number of non-zero weights After all four weighted standard deviations were computed, a statistical test, a Z-test to be more specific due to the large sample size, was conducted to determine if there were any differences between the average IRI increase per year among the combinations of pavement type and priority system. The Z-test compared the difference between the two weighted slopes against the product of the weighted standard deviation and critical value (Z /2). Since a 90% confidence level was used, the corresponding Z /2 value of was used. If the difference in slope were greater than the product of the weighted standard deviation and the critical value, then the two slopes would have a statistically significant difference at 90% confidence. If the difference were less than the product, then there would be no statistical difference. All results regarding the pavement type and priority system comparisons are listed under Chapter 4.2 on page

55 The next step of the study consisted of categorizing the 12 Ohio Districts into four regions (listed in Table 3.2) based average annual snowfall in inches per year, shown in the following table in reference to Figure 3-4. The Z-test analyses at 90% confidence level were performed, similarly to the previous comparisons, to determine if there exists a statistically significant difference between the yearly average increase of RIRI in the four regions. The results regarding all comparisons are listed under Table 4.2 on page 49. Table 3.2 Ohio Snowfall Regions < 20 inches/year 20~30 inches/year 30~40 inches/year 40 inches/year District # 8, 9, 10 1, 5, 6, 7, 11 2, 3 4, 12 Figure 3-4 Ohio Annual Snowfall with District Number 39

56 To further refine and show what extreme annual snowfall could affect pavement roughness on different pavement-priority combinations, two levels of annual snowfall threshold were used, listed in Tables 3.3 and 3.4: one at 30 inches per year, the other at 40 inches per year. Under these thresholds and separated by the aforementioned pavement-priority combinations, each district s data was combined as one dataset and Z-tests were performed again at 90% confidence level to distinguish any difference in average annual increase in RIRI. These results are listed in Table 4.6 on page 50. Table inch Snowfall Threshold on Ohio Districts < 30 inches/year 30 inches/year District # 1, 5, 6, 7, 8, 9, 10, 11 2, 3, 4, 12 Table inch-per-year Snowfall Threshold on Ohio Districts < 40 inches/year 40 inches/year District # 1, 2, 3, 5, 6, 7, 8, 9, 10, 11 4, RIRI Deterioration by Frequency Rather than looking for RIRI patterns at the route-section level by method of regression, a frequency method across the entire pavement-priority network was additionally implemented on composite-priority and flexible-general highways since they represented a contrast in pavement type as well as priority system while still accumulating a majority of Ohio s highway my mileage. Rather than averaging all route sections regression 40

57 slopes, this method averaged all the RIRI differences between successive years, producing a much larger sample size. This method was different since these differences were either positive, negative, or zero, indicating that the pavements have either deteriorated, improved, or remained unchanged, respectively. Regular intervals were applied to categorize the ranges of RIRI. Histograms were then plotted to show the frequencies the average annual increase in RIRI each type of highway experienced. Results are listed in Table 4.12 on page

58 Chapter 4 Results and Discussions 4.1 Results of Ohio PCR- RIRI Correlations Table 4.1 below shows the correlation summary of the change in Pavement Condition Rating ( PCR) and change in right-wheel International Roughness Index ( RIRI) for all general system highways with flexible pavements across Ohio. These correlations are also plotted in the five following figures. Table 4.1 Summary of PCR- RIRI Correlations Correlation Period Years Compared Correlation Coefficient Sample Size 1-year 2013 vs ,235 2-year 2013 vs ,235 3-year 2013 vs ,749 5-year 2013 vs , year 2013 vs ,322 The 1-year correlation, plotted in Figure 4-1, yielded a correlation coefficient of just This extremely low value with a large sample size of 5,235 suggested that there were little to no relations between pavement distresses and ride quality (roughness) 42

59 PCR between 2012 and 2013, which was expected since pavement condition and roughness index typically do not deteriorate quickly in such a short amount of time yr. Correlation on General-Flexible Highways IRI Figure year PCR- RIRI Correlation The 2-year and 3-year correlations yielded slightly better but still weak correlation coefficients of and 0.187, respectively. Similar to the 1-year correlation, this suggested that pavement condition had very little ties with pavement roughness during the first three years after the pavements are constructed or rehabilitated. However, the 5-year correlation produced a better coefficient of Although the coefficient more than doubled in comparisons with previous ones, however it is still much less than 0.8 for PCR to have a strong correlation with RIRI. Because of this sudden increase, it was 43

60 PCR reasonable to assume that a five year span was needed for pavement surface to have an effect on ride quality yr. Correlation on General-Flexible Highways IRI Figure year PCR- RIRI Correlation 44

61 PCR PCR 60 3-year Correlation on General-Flexible Highways RIRI Figure year PCR- RIRI Correlation 60 5-year Correlation on General-Flexible Highways RIRI Figure year PCR- RIRI Correlation 45

62 PCR year Correlation on General-Flexible Roads RIRI Figure year PCR- RIRI Correlation As for the 10-year correlation, the coefficient had decreased to 0.286, demonstrating again that pavement conditions did not have much of an effect on ride quality. This could be due to the limited data available on identical sections of routes spanning a decade apart, hence the significant drop in sample size compared to the 1- and 2-year correlations. These weak PCR- RIRI correlations on general-flexible highways were likely to be caused by three reasons: data used, PCR subjectiveness, and the nature of their measurements. First, all segments of general-flexible highway in years 2003, 2008, 2010, 2011, 2012, and 2013 were used without accounting for pavement reconstructions or rehabilitations. It is very likely that none of the PCR and IRI values used in this study were taken at the same time. For example, a PCR of a severely deteriorated section could 46

63 have been used to compare against the IRI value of the same section after reconstruction a year later, creating an outlier in the dataset and ultimately lowering the correlation coefficient. Secondly, Pavement Condition Rating is still ultimately a subjective form of rating. In other words, it is still user-based even given a detailed procedure and specifications of distresses and severity. One mistake on classifying the distress or quantifying the severity could drastically change the overall PCR score. Lastly, this correlation suggested that PCR and RIRI are two totally different measures. While RIRI measures what a rider feels along a road profile, PCR is what engineers measured. Therefore, rater and rating consistencies are keys to express a true PCR. On the other hand, International Roughness Index (IRI) is based on the concept of the quarter-car model. IRI is stable with time because it is based on the concept of a true longitudinal profile rather than the physical properties of a particular type of instrument. 4.2 Snowfall and Pavement-Priority Deterioration Comparisons Table 4.2 below shows the average right-wheel International Roughness Index (RIRI) increase per year on priority system on composite highways throughout Ohio along with the number of sample size used as well as the weighted standard deviations. As expected, the region with the highest snowfall (i.e. Districts 4 and 12) experienced the greatest annual average RIRI increase, while the other three showed no statistical differences when compared with one another at 90% confidence level. This suggested that snowfall less than 40 inches per year had no effects on pavement roughness on composite-priority pavements. 47

64 Table 4.2 Summary of Weighted RIRI Trend on Composite-Priority Regions Annual Snowfall Annual Average RIRI Weighted (inches/year) Increase (inches/mile) Standard Deviation Sample Size < ~ ~ Table 4.3 Statistical Differences among Snowfall Regions on Composite-Priority < 20 20~30 30~40 40 < 20 20~30 No 30~40 No No 40 Yes Yes Yes As for the general system on flexible pavements, the results are listed in Tables 4.4. Districts 2 and 3 having an annual snowfall of 30 to 40 inches per year yielded the lowest increase in RIRI, at inches per mile. Instead of areas with the highest amount of snow (i.e. Districts 4 and 12), regions with an annual snowfall of 20 to 30 inches resulted the highest increase in RIRI, at inches per mile. This might be due to the fact that it had nearly three times more data than the next region. Furthermore, nearly all combinations of snowfall amounts listed in Table 4.5 presented statistical differences with 90% confidence, except for the most extreme regions: less than 20 versus more than 40 inches of snow per year. One explanation for this could be freeze-thaw cycles. When water freezes, it expands. As the water in the moist pavement freezes, it produces 48

65 pressure in the pores. If this pressure exceeds the tensile strength of the pavement, the cavity will dilate and rupture. The accumulative effect of successive free-thaw cycles and disruption of paste and aggregate could eventually cause expansion and cracking. Less snow could mean less freeze-thaw cycles and less pavement expansion, thus no difference in RIRI increase was found. Table 4.4 Summary of Weighted RIRI Trend on Flexible-General Regions Annual Snowfall Annual Average RIRI Weighted (inches/year) Increase (inches/mile) Standard Deviation Sample Size < ~ ,440 30~ Table 4.5 Statistical Differences among Flexible-General Snowfall Regions < 20 20~30 30~40 40 < 20 20~30 Yes 30~40 Yes Yes 40 No Yes Yes In terms of a snowfall threshold for which pavement roughness showed a different roughness increase per year, the results are listed in Tables 4.6 and 4.7. Unsurprisingly all combinations of pavement-priority with more than 30 inches of annual snowfall experienced a higher annual average increase in RIRI than those do not, with the 49

66 exception of the general system of flexible pavements. Again, the number of freeze-thaw cycles could have been a factor. Also, the unevenness in sample size due to district exclusivity could have also skewed the results. Table 4.6 Statistics of RIRI Trends for Snowfall < 30 in/yr. Pavement- Priority Composite- Priority Composite- General Flexible- Priority Flexible- General Annual Average RIRI Increase (inches/mile) Weighted Standard Deviation Sample Size ,916 Table 4.7 Statistics of RIRI Trends for Snowfall 30 in/yr. Pavement- Priority Composite- Priority Composite- General Flexible- Priority Flexible- General Annual Average RIRI Increase (inches/mile) Weighted Standard Deviation Sample Size In reference to Table 4.8, all pavement-priority combinations that received less than 30 inches of snowfall per year had an expected lower RIRI increase than those above the 50

67 threshold, with the exception of the priority system on flexible pavements, which showed no difference. This implied that an annual snowfall of 30 inches or less did not affect the pavement roughness on flexible pavements with priority system. Table 4.8 Statistical Difference of RIRI Trends at Snowfall Threshold of 30 inches/year Pavement Type & Priority System Combination Composite-Priority Composite-General Flexible-Priority Flexible-General Statistical Difference? Yes Yes No Yes The results for the most extreme snowfall threshold, at 40 inches per year, are listed in Tables 4.9 through Similar to the 30-inch predecessor, they again showed that an increase of snowfall correlated to an increase in RIRI across pavement-priority combinations except for flexible-general. Again, the extreme contrast in sample size due to district exclusivity could have been the reason why a statistical difference in annual average RIRI increase was not found with areas more and less than 40 inches annually. 51

68 Table 4.9 Statistics of RIRI Trend for Snowfall of < 40 inches/year Pavement- Priority Composite- Priority Composite- General Flexible- Priority Flexible- General Annual Average RIRI Increase (inches/mile) Weighted Standard Deviation Sample Size , ,384 Table 4.10 Statistics of RIRI Trend for Snowfall of 40 inches/year Pavement- Priority Composite- Priority Composite- General Flexible- Priority Flexible- General Annual Average RIRI Increase (inches/mile) Weighted Standard Deviation Sample Size Table 4.11 Statistical Difference on RIRI Trend at Snowfall Threshold of 40 inches/year Pavement Type & Priority System Combination Composite-Priority Composite-General Flexible-Priority Flexible-General Statistical Difference? Yes Yes Yes No 52

69 Frequency The RIRI frequency plot and a summary of the trends for composite-priority pavements at a threshold of 30 inches of snowfall annually are shown in the following figure and table. The figure illustrates that both pavements, either below or above the threshold, have very similar frequencies in RIRI, no matter the range. It also shows that sections of highway would most likely experience no change in pavement roughness between successive years; however, if the pavement does deteriorate, the table shows that its RIRI value would increase by an average of and inches per mile following a year of below and above 30 inches of snow, respectively. 60% 50% Frequency of RIRI on Composite-Priority < 30 inches/year >= 30 inches/year 40% 30% 20% 10% 0% More RIRI Figure 4-5 Frequency Plot of Composite-Priority Highways at 30-inch Snow Threshold 53

70 Table 4.12 RIRI Trends by Method on Composite-Priority Pavements Method < 30 inches/year 30 inches/year Annual RIRI Sample Size Annual RIRI Sample Size Slope Regression Frequency , ,408 As for the same snowfall threshold applied on flexible-general highways, similar trends were found. No matter the amount of snowfall, most sections of pavements had no roughness increase more than 50% of the time, and there are no major differences in frequencies across any RIRI range, as shown in Figure 4-6. However, when a flexiblegeneral pavement deteriorates, a difference was found based on snowfall. By both the slope regression and frequency methods, pavements that received less snow had a higher average annual increase in RIRI than those who received more snow as Table 4.13 indicates. This was counterintuitive and could be explained by additional confounding factors such as freeze-thaw cycles, soil type, and traffic volume. Therefore, future studies need to be conducted. 54

71 Frequency 60% 50% Frequency of RIRI on Flexible-General < 30 inches/year >= 30 inches/year 40% 30% 20% 10% 0% RIRI Figure 4-6 Frequency Plot of General-Flexible Highways at 30-inch Snow Threshold Table 4.13 RIRI Trends by Method on Flexible-General Pavements Method < 30 inches/year 30 inches/year Annual RIRI Sample Size Annual RIRI Sample Size Slope Regression , Frequency , ,891 Two extreme cases of snowfall scenario were also studied where the effects of less than 20 and over 40 inches of snow annually on RIRI trends were compared on both composite-priority and flexible-general highways. Their frequency plots as well as their slope regression comparisons are shown and listed in the next few pages. 55

72 Frequency 60% 50% FrequencyPlot of Composite-Priority Highways < 20 inches/year >= 40 inches/year 40% 30% 20% 10% 0% RIRI Figure 4-7 Extreme Snowfall RIRI Frequency Plot on Composite-Priority Highways Table 4.14 Extreme Snowfall RIRI Trends on Composite-Priority Pavements Method < 20 inches/year 40 inches/year Annual RIRI Sample Size Annual RIRI Sample Size Slope Regression Frequency , ,012 Overall, no drastic differences in frequency of RIRI were found between the two snowfall regions. Both regions had little to no change in roughness between successive years based on their frequency. The only major difference was the large increase in annual RIRI. Highways that had less snow had an average RIRI increase of about to inches per mile depending on the method used, while highways had the most snow had an expected increase in RIRI, at and inches per mile, respectively. 56

73 Frequency For the extreme snowfall regions on flexible-general highways, again no major differences were found as far as frequencies. With nearly 55% of the time, both regions were expected to have no change in RIRI between successive years by the frequency method. Both regions indicated that there were positive correlations between annual snowfall amount and RIRI increase, as Table 4.15 indicates below. 60% 50% Frequency Plot of Flexible-General Highways < 20 inches/year >= 40 inches/year 40% 30% 20% 10% 0% RIRI Figure 4-8 Extreme Snowfall RIRI Frequency Plot on General-Flexible Highways Table 4.15 Extreme Snowfall RIRI Trends on General-Flexible Pavements Method < 20 inches/year 40 inches/year Annual RIRI Sample Size Annual RIRI Sample Size Slope Regression Frequency , ,763 57

74 4.3 Summary of Findings In summary, results through the slope regression method showed a positive correlation between annual snowfall and average annual increase in RIRI in composite-priority, composite-general, as well as flexible-priority highways as shown in Table Those that received less than 30 inches of snow had an average RIRI increase of 4.570, 5.765, and inches per mile, while those received more than 30 inches increased to 5.228, 6.265, and inches per mile, respectively. These effects were magnified once the snowfall threshold was increased to 40 inches per year. Flexible-general was the only pavement-priority combination that showed a counterintuitive, negative correlation. As stated previously, this may be due to large differences in sample size as well as the number of freeze-thaw cycles. Table 4.16 Summary of Snowfall Thresholds on Pavement Roughness Pavement-Priority < 30 RIRI 30 RIRI < 40 RIRI 40 RIRI Combination (inches/mile) (inches/mile) (inches/mile) (inches/mile) Composite-Priority Composite-General Flexible-Priority Flexible-General Results from Table 4.17 indicated that composite-priority highways have a clear snowfall threshold of 40 inches annually since regions having less than 20, between 20 and 30, and between 30 and 40 inches per year showed no statistical differences in terms of RIRI. But the region that was equal to or greater than 40 inches per year, the RIRI showed a 58

75 sharp increase. As for flexible-general highways, unlike composite-priority, no logical relationships were found between annual snowfall and pavement roughness since the region having the second least amount of snow per year had the highest amount of increase in RIRI, and the region that had the second most amount of snow produced the least amount of RIRI increase. Table 4.17 Summary of Snowfall Regions on Pavement Roughness Pavement- Priority Combination Composite- Priority Flexible- General Snowfall Region (in/year) RIRI by Slope Regression (in/mile) Statistical Differences? < 20 20~30 30~40 40 < ~ No ~ No No Yes Yes Yes - < ~ Yes ~ Yes Yes No Yes Yes - The frequency method produced much more clear results since it showed that higher snowfall led to higher RIRI in all pavement-priority combinations, except for flexiblegeneral highways, as indicated in Table The results from the frequency method produced higher RIRI averages than the slope regression method, which was valid. Since higher slopes generally occurred in shorter sections (e.g. potholes only exist in a relatively small section rather than throughout the entire road), therefore once the lengths 59

76 of segments were factored in, this weighted RIRI was ultimately lowered when combined with longer segments. Table 4.18 Method of RIRI Trends Comparisons Pavement-Priority Combination Composite-Priority Flexible-General Method < 30 RIRI (in./mi) 30 RIRI (in./mi) < 20 RIRI (in./mi) 40 RIRI (in./mi) Slope Regression Frequency Slope Regression Frequency

77 Chapter 5 Conclusions and Recommendations 5.1 Summary and Conclusions As pavement conditions in the highway system across the nation continue to deteriorate, it is crucial for government agencies and engineers to analyze and predict pavement deterioration patterns so that federal, state, and other agencies can prioritize any reconstruction or rehabilitation and allocate resources appropriately. This thesis studied the relationship among the combinations of pavement types and priority systems and the effects they had on average annual increase in right-wheel International Roughness Index (RIRI) under certain annual snowfall regions within the Ohio highway system. Two prediction methods were used in three scenarios of snowfall to study the snowfall- RIRI relationship: one was split into four snowfall regions, and the other two were by different amount of snowfall thresholds 30 and 40 inches per year. Both the slope regression and frequency methods confirmed that all regions with higher snowfall corresponded to higher average annual increase in RIRI in all pavement-priority combinations, except for flexible-general. 61

78 These RIRI trends are useful for officials in estimating the cost to achieve and sustain pavement performance targets and recommending allocations of available maintenance funds across districts. Thus, pavement roughness data are an important input into the pavement management system to develop estimates of pavement maintenance and rehabilitation needs based on an optimization analysis. These RIRI trends are also beneficial when it comes to identifying and prioritizing recommended pavement sections for preventative maintenance activities. 5.2 Recommendations for Future Research This thesis can be further improved in ways by incorporating additional variables such as soil type, truck average daily traffic, average annual daily traffic (AADT), equivalent single-axle load (ESAL), and pavement thickness. In additional, other environmental factors such as annual rainfall and number of freeze-thaw days can also be added to create a much more comprehensive model. And to eliminate confounding datasets, a more extensive statistical model such as the analysis of variance (ANOVA) is recommended. 62

79 References AASHTO (2003). Highway Drainage Guidelines. American Association of State Highway and Transportation Officials. Washington, D.C. Al-Omari, B. & Darter, M. (1994). Relationships between International Roughness Index and Present Serviceability Rating. Transportation Research Record, 1435, National Academy of Sciences, Washington, D.C. Alkire, B. (2009). Pavement Condition Indices. Michigan Technological University, Civil and Environmental Engineering. Retrieved from Arellano, J., de Farias, M.M., de Souza, R.O., Ganesan, V.P.K., McDonald, M.P., Morian, D., Neto, S.D., Smith, J.T., Stoeffels, S.M., & Tighe, S.L. (2006). Improving Pavements with Long-Term Pavement Performance: Products for Today and Tomorrow. FHWA-HRT Office of Research, Development, and Technology, Office of Infrastructure, Federal Highway Administration, U.S. Department of Transportation, McLean, VA ASTM International (2008). ASTM E , Standard Practice for Computing International Roughness Index of Roads from Longitudinal Profile Measurements, ASTM International, West Conshohocken, PA Carey, W.N. & Irick, P.E. (1960). The Pavement Serviceability-Performance Concept. Highway Research Board Bulletin 250. Federal Highway Administration (1990). Highway Performance Monitoring System, Field Manual, Appendix J. FHWA Publication A. U.S. Department of Transportation, Washington, D.C. 63

80 Federal Highway Administration (2008). Concrete Pavement Technology Update. U.S. Department of Transportation, Washington, D.C. Retrieved from Federal Highway Administration (2012). Transportation Asset Management Case Studies The Ohio Experience. U.S. Department of Transportation, Washington, D.C. Retrieved from Federal Highway Administration (2015). Smoothness. U.S. Department of Transportation, Washington, D.C. Retrieved from Gaspard K., Martinez M., Zhang Z., & Wu Z. (2006). Impact of Hurricane Katrina on Roadways in the New Orleans Area, Louisiana Department of Transportation and Development, Louisiana Transportation Research Center, Baton Rouge, LA Gulen, S., Zhu, K., Shan, J., & Flora, W.F. (2001). Development of Improved Pavement Performance Prediction Models for the Indiana Pavement Management Systems. FHWA/IN/JTRP-2001/17. Program Development Division and Research Division, Indiana Department of Transportation, Indianapolis, IN Haas, R. (2003). Good technical foundations are essential for successful pavement management. Proceedings of MAIREPAV 03. Guimaraes, Portugal Highway Research Board (1972). Skid Resistance. National Cooperative Highway Research Program: Synthesis of Highway Practice (14). Highway Research Board, National Academy of Sciences, Washington, D.C. Hveem, F.N. & Carmany, R.M. (1948). The Factors Underlying a Rational Design of Pavements. Highway Research Board Proceedings (28). National Academy of Sciences, Washington, D.C. The Indiana Design Manual (2006). Pavement and Underdrain Design Elements. Retrieved from 06%20English/ 64

81 Jackson, N. & Puccinelli, J. (2006). Long-Term Pavement Performance (LTPP) Data Analysis support: National Pooled Fund Study TPF-5(013), Effects of Multiple Freeze Cycles and Deep Frost Penetration on Pavement Performance and Cost. FHWA-HRT Research, Development, and Technology, Turner-Fairbank Highway Research Center, McLean, VA Kennedy, W. & Hager A. (2008). Severe Winter Weather Impacts on Urban Pavements. 7 th International Conference on Managing Pavement Assets, Canada Lefler, N., Council, F., Harkey, D., Carter, D., McGee, H., & Daul, M. (2010). Model Inventory of Roadway Elements MIRE, Version 1.0. FHWA-SA Office of Safety, Federal High Administration, Washington, D.C. Li, W., Mills L., & McNeil S. (2011). The Implications of Climate Change on Pavement Performance and Design. University Transportation Center, University of Delaware, Newark, DE Martin T.C. (1996). A Review of Existing Pavement Performance Relationships. ARRB Transport Research, (282) Report, Vermont South, Victoria, Australia Minnesota Department of Transportation (2006). An Overview of MnDOT s Pavement Condition Rating Procedures and Indices, Pavement Management Unit, Minnesota Department of Transportation, St. Paul, MN. Retrieved from Minnesota Department of Transportation (2011). MnDOT Pavement Distress Identification Manual. Pavement Management Unit, Office of Materials and Road Research, Minnesota Department of Transportation, St. Paul, MN. Retrieved from Nasimifar, S.M., Pouranian, M.R., & Azadi, M. (2011). The Effect of Climatic Conditions of Various Regions of Iran on Pavement Fatigue Cracking. ACEEE International Journal on Transportation and Urban Development, 01 (01) 27-33, Tehran, Iran 65

82 Ohio Department of Transportation (2006). Pavement Condition Rating System. Office of Planning, Division of Planning, Ohio Department of Transportation, Columbus, OH. Retrieved fromhttps:// TIM/Documents/PCRManual/2006PCRManual.pdf Ohio Department of Transportation (2011). Snow & Ice Practices. Division of Operations, Office of Maintenance Administration, Ohio Department of Transportation, Columbus OH. Retrieved from Divisions/Operations/Maintenance/SnowandIce/Snow%20and%20Ice%20Best%2 0Practices/ODOT%20Snow%20and%20Ice%20Practices%20March% pdf Ohio Department of Transportation (2014). 5 Criteria for Pavement Condition Ratings. Office of Technical Services, Ohio Department of Transportation, Columbus, OH. Retrieved from Documents/PCR_Documents/5%20Criteria%20for%20Pavement%20Condition% 20Ratings.pdf Ohio Department of Transportation (2014). Centerline Miles, Lane Miles, and VMT by Route and County, Report RI-82B. Office of Technical Services, Division of Planning, Ohio Department of Transportation, Columbus, OH. Retrieved from /RI82B.pdf Ohio Department of Transportation (2014). Pavement Design Manual. Office of Pavement Engineering, Ohio Department of Transportation, Columbus, OH. Retrieved from Pavement%20Design%20%20Rehabilitation%20Manual/Complete_PDM_ _version.pdf Pavement Interactive (2006). Present Serviceability Index. Retrieved from Pavement Interactive (2007). Roughness. Retrieved from Pavement Interactive (2012). What Is Mechanistic-Empirical Design? The MEPDG and You. Retrieved from 66

83 Ping, W.V. & Yunxia, H. (1998). Evaluation of Flexible Pavement Performance Life in Florida. FL/DOT/RMC/0670 (2) College of Engineering, Florida A&M University and Florida State University, Tallahassee, FL. Prozzi, J.A. (2001). Modeling Pavement Performance by Combining Field and Experimental Data. Dissertation, UC Transportation Center, University of California, Berkeley, CA. Retrieved from diss066.pdf Reza, F., Boriboonsomsin, K., & Bazlamit, S.M. (2005). Development of a Composite Pavement Performance Index. ST/SS/ Ohio Department of Transportation, Columbus, OH Saba, R.G., Huvstig, A., Hildebrand, G. Sund, E., Evensen, R., Sigursteinsson, H., & Elsander, J. (2006). Performance Prediction Models for Flexible Pavements: A State-of-the-Art Report. Nordic Road and Transport Research, 1 Sayers, M.W., Gillespie, T.D., & Paterson, W.D.O. (1986). Guidelines for Conducting and Calibrating road Roughness Measurements. World Bank Technical Paper Number 46, p. 87, The World Bank, Washington, D.C. Sayers, M.W. & Karamihas, S.M. (1998). The Little Book of Profiling: Basic Information about Measuring and Interpreting Road Profiles, The Regent of the University of Michigan Schaefer, V., Stevens, L., White, D., & Ceylan, H. (2008). Design Guide for Improved Quality of Roadway Subgrades and Subbases. Iowa Highway Research Board (Project TR-525) Shahin, M.Y. (1994). Pavement Management for Airports, Roads and Parking Lots. Norwell, MA: Kluwer Academic Publishers. Shamsabadi, S.S., Tari, Y.S.H., Birken, R., & Wang, M. (2014). Deterioration Forecasting in Flexible Pavements Due to Floods and Snow Storms. 7 th European Workshop on Structural Health Monitoring: La Cite, Nantes, France 67

84 Smith, J.T., Tighe, S.L., Andrey, J.C., & Mills, B. (2008). Temperature and Precipitation Sensitivity Analysis on Pavement Performance, Transportation Research Circular, (E-C126) p University of Michigan Transportation Research Institute (2002). The Shape of Roads to Come: Measuring and Interpreting Road Roughness Profiles. UMTRI Research Review, 33 (1) Wang, H. (2006). Road Profiler Performance Evaluation and Accuracy Criteria Analysis. Thesis, Virginia Polytechnic Institute and State University 68

85 Appendix A ODOT Pavement Condition Rating Procedure Figure A-1 ODOT Pavement Condition Rating Scale (ODOT, 2006) 69

86 Table A.1 Flexible Pavement Condition Rating Form Descriptions (ODOT, 2006) 70

87 Table A.2 Flexible Pavement Condition Rating Form with Deduct Values (ODOT, 2006) 71

88 Table A.3 Asphalt Surface Local Pavement Condition Rating Form Descriptions (ODOT, 2006) 72

89 Table A.4 Asphalt Surface Local Pavement Condition Rating Form with Deduct Values (ODOT, 2006) 73

90 Table A.5 Composite Pavement Condition Rating Form Descriptions (ODOT, 2006) 74

91 Table A.6 Composite Pavement Condition Rating Form with Deduct Values (ODOT, 2006) 75

92 Table A.7 Jointed Concrete Pavement Condition Rating Form Descriptions (ODOT, 2006) 76

93 Table A.8 Jointed Concrete Pavement Condition Rating Form with Deduct Values (ODOT, 2006) 77

94 Table A.9 Continuously Reinforced Concrete Pavement Condition Rating Form Descriptions (ODOT, 2006) 78

95 Table A.10 Continuously Reinforced Concrete Pavement Condition Rating Form with Deduct Values (ODOT, 2006) 79

96 Table A.11 Brick Paver Condition Rating Form Descriptions (ODOT, 2006) 80

97 Table A.12 Brick Paver Condition Rating Form with Deduct Values (ODOT, 2006) 81

98 Appendix B MnDOT Pavement Distress Types, Severities, and Weighing Factors Table B.1 Bituminous Pavement Surface Rating Weighting Factors (MnDOT, 2011) Distress Type Severity Weighting Factor Low 0.01 Transverse Cracking Medium 0.1 High 0.2 Longitudinal Cracking Longitudinal Joint Deterioration Low 0.02 Medium 0.03 High 0.04 Low 0.02 Medium 0.03 High 0.04 Multiple (block) Cracking Alligator Cracking Rutting Raveling & Weathering Patching

99 Table B.2 Concrete Pavement Surface Rating Weighting Factors (MnDOT, 2011) Distress Type Severity Weighting Factor Low 0.10 Transverse Joint Spalling High 0.20 Longitudinal Joint Spalling Low 0.10 High 0.20 Cracked Panels Broken Panels Faulted Jointed Faulted Panels % Overlaid Panels Patched Panels D-Cracking Table B.3 Continuously Reinforced Concrete Pavement Surface Rating Weighting Factors (MnDOT, 2011) Distress Type Severity Weighting Factor Patch Deterioration Localized Distress D-Cracking Transverse Cracking

100 Appendix C ODOT Pavement-Priority Highways Figure C-1 ODOT Composite Pavement General System Highways 84

101 Figure C-2 ODOT Composite Pavement Priority System Highways 85

102 Figure C-3 ODOT Composite Pavement Urban System Highways 86

103 Figure C-4 ODOT Flexible Pavement General System Highways 87