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Development of a direct type road roughness evaluation system

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Title:
Development of a direct type road roughness evaluation system
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English
Creator:
Hu, Fengxuan
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University of South Florida
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Tampa, Fla.
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Subjects / Keywords:
RN
IRI
profile
correlation
repeatability
walking profiler
Dissertations, Academic -- Civil Engineering -- Masters -- USF   ( lcsh )
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government publication (state, provincial, terriorial, dependent)   ( marcgt )
bibliography   ( marcgt )
theses   ( marcgt )
non-fiction   ( marcgt )

Notes

Summary:
ABSTRACT: Roughness is an important indicator of pavement riding comfort and safety. It is a condition indicator that should be carefully considered when evaluating primary pavements. At the same time, the use of roughness measurements plays a critical role in the pavement management system. There are many devices used for roughness evaluation. The major tools used for road roughness quantify are the road profilers. In the thesis research, in order to obtain useful pavement surface condition data for pavement evaluation, two direct type road roughness evaluation systems were developed with the combination of modern sensor technology and computer technology. The thesis will focus on the development of the direct type profiler systems, including the improvement of the hardware design, the new direct type road roughness-measuring system using different method, the software development, which makes it more functional. In order to evaluate the accuracy and correction of the direct type profiler system, different roughness devices (including FDOT High-Speed laser profiler, FACE Dipstick and direct type I profiler) were operated in 4 calibration sites. The research focused on several performance measures, such as correlativity, repeatability. IRI and RN results from these devices were analyzed to evaluate the correlativity between these devices. After verified that direct type I profiler has good repeatability and correlation with FDOT High-Speed laser profiler, FACE Dipstick, 10 calibration sites data in Tampa were collected using direct type I profiler and direct type II profiler. The repeatability and correlation analysis between the two profilers were performed. From field experiments and data analysis, it shows: 1.Direct type I profiler showed satisfactory repeatability performances; 2.Direct type I profiler has good RN correlations High-Speed laser profiler; 3.Direct type I profiler has good correlations with Dipstick, High-Speed laser profiler in terms of IRI 4.Direct type II profiler does not has good correlation with direct type I profiler; the performance needs to be improved. Except for these conclusions, it is also found that the High-Speed profiler can be operated at different speeds with little differences in RN values, the sampling rate did show impact on RN value.
Thesis:
Thesis (M.S.C.E.)--University of South Florida, 2004.
Bibliography:
Includes bibliographical references.
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Mode of access: World Wide Web.
Statement of Responsibility:
by Fengxuan Hu.
General Note:
Title from PDF of title page.
General Note:
Document formatted into pages; contains 96 pages.

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aleph - 001469411
oclc - 55731188
notis - AJR1165
usfldc doi - E14-SFE0000313
usfldc handle - e14.313
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ABSTRACT: Roughness is an important indicator of pavement riding comfort and safety. It is a condition indicator that should be carefully considered when evaluating primary pavements. At the same time, the use of roughness measurements plays a critical role in the pavement management system. There are many devices used for roughness evaluation. The major tools used for road roughness quantify are the road profilers. In the thesis research, in order to obtain useful pavement surface condition data for pavement evaluation, two direct type road roughness evaluation systems were developed with the combination of modern sensor technology and computer technology. The thesis will focus on the development of the direct type profiler systems, including the improvement of the hardware design, the new direct type road roughness-measuring system using different method, the software development, which makes it more functional. In order to evaluate the accuracy and correction of the direct type profiler system, different roughness devices (including FDOT High-Speed laser profiler, FACE Dipstick and direct type I profiler) were operated in 4 calibration sites. The research focused on several performance measures, such as correlativity, repeatability. IRI and RN results from these devices were analyzed to evaluate the correlativity between these devices. After verified that direct type I profiler has good repeatability and correlation with FDOT High-Speed laser profiler, FACE Dipstick, 10 calibration sites data in Tampa were collected using direct type I profiler and direct type II profiler. The repeatability and correlation analysis between the two profilers were performed. From field experiments and data analysis, it shows: 1.Direct type I profiler showed satisfactory repeatability performances; 2.Direct type I profiler has good RN correlations High-Speed laser profiler; 3.Direct type I profiler has good correlations with Dipstick, High-Speed laser profiler in terms of IRI 4.Direct type II profiler does not has good correlation with direct type I profiler; the performance needs to be improved. Except for these conclusions, it is also found that the High-Speed profiler can be operated at different speeds with little differences in RN values, the sampling rate did show impact on RN value.
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Development Of A Direct Type Road Roughness Evaluation System by Fengxuan Hu A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Civil Engineering Department of Civil and Environmental Engineering College of Engineering University of South Florida Major Professor: Jian John Lu, Ph.D. Manjriker Gunaratne, Ph.D. Ram Pendyala, Ph.D. Date of Approval: April 1, 2004 Keywords: profile, iri, rn, walking profiler, repeatability, correlation Copyright 2004 Fengxuan Hu

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ACKNOWLEDGMENTS I am here to show my sincere gratitude to my major professor, Dr. Jian John Lu for his guidance in my thesis. I am also grat eful to Dr. Manjriker Gunaratne, and Dr. Ram Pendyala for their instructions to my thesis and graduate studies. Thanks also go to Mr. Mr. Shitao Gu, Mr. Pan Liu, for their help during the project.

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DEDICATION This work is dedicated to my wife, H ong Xu, for her love and support through the years.

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i TABLE OF CONTENTS LIST OF TABLES iii LIST OF FIGURES iv ABSTRACT viii CHAPTER 1 INTRODUCTION 1 1.1 Research Background 1 1.2 Basic Concepts 3 1.2.1 Profiles 3 1.2.2 Profile Index 4 1.2.3 Roughness Definition 5 1.2.3.1 International Roughness Index (IRI) 6 1.2.3.2 Ride Number (RN) 6 1.2.4 Signal Processing Filter 7 1.3 Research Objective 7 1.4 Outline of the Thesis 9 CHAPTER 2 LITERATURE REVIEW 10 2.1 Overview 10 2.2 Roughness Measurement Systems 12 2.2.1 Class I System 13 2.2.1.1 Rod and Level 13 2.2.1.2 Dipstick 14 2.2.2 Class II System 15 2.2.2.1 K.J.Law Profilometer 15 2.2.2.2 APL Profilometer 16 2.2.2.3 South Dakota Profiler 17 2.2.3 Class III System 17 2.2.3.1 BPR Roughometer 18 2.2.3.2 Light Weigth Profiler 19 2.2.3.3 Laser Profiler 20 2.3 Roughness Indices 23 CHAPTER 3 METHODOLOGY 26 3.1 Introduction 26

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ii 3.2 Methods to Get Road Profiles 26 3.2.1 Method Used in Direct Type I Profiler 26 3.2.2 Method Used in Direct Type II Profiler 28 3.3 IRI and RN Models and Algorithms 29 3.3.1 Quarter Car Model 30 3.3.2 Calculation of IRI 33 3.3.3 Calculation of RN 37 3.4 Digital Filter 39 CHAPTER 4 SYSTEM DEVELOPMENT 43 4.1 System Requirements 43 4.2 Direct Type Profiler Hardware Development 45 4.2.1 Direct Type Profiler Introduction 45 4.2.2 Direct Type I Profiler Hardware 47 4.2.2.1 Sensors and Conversion Module 48 4.2.2.2 Interface 48 4.2.3 Direct Type II Profiler Hardware 49 4.2.3.1 Sensors 50 4.2.3.2 Interface 50 4.3 Profiler Software Development 51 4.3.1 Overview 51 4.3.2 Data Sampling Module 52 4.3.3 Data Processing Module 53 4.3.4 Calibration and Configuration Module 56 4.3.4.1 System Calibration 56 4.3.4.2 Draft Calibration 56 4.3.4.3 System Configuration 57 CHAPTER 5 DATA COLLECTION AND DATA ANALYSIS 58 5.1 Data Collection 58 5.2 Data Analysis 61 5.2.1 Repeatability Analysis 61 5.2.2 Correlation Analysis 63 5.2.2.1 RN Correlation Analysis 63 5.2.2.2 IRI Correlation Analysis 69 5.3 Analysis between Direct Type I and Direct Type II Profilers 76 5.3.1 Repeatability 77 5.3.2 Correlation Analysis 78 CHAPTER 6 SUMMARY, CONCLUSION AND RECOMMENDATION 80 6.1 Summary 80 6.2 Conclusions 82 6.3 Recommendations 83 REFERENCES 84

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iii LIST OF TABLES Table 5-1 Test Site Locations 60 Table 5-2 RN Values of Direct Type I Profiler in Each Section 61 Table 5-3 IRI Values of Direct Type I Profiler in Each Section 62 Table 5-4 RN Values between Repeated Runs of FDOT High-Speed Profiler 62 Table 5-5 R2 Values at Different Operating Speeds and Sampling Rates 69 Table 5-6 IRI Values Collected By FACE Dipstick and Direct Type I Profiler 70 Table 5-7 RN and IRI Value of Direct Type I Profiler 77 Table 5-8 RN and IRI Value of Direct Type II Profiler 78 Table 5-9 RN and IRI Values Collected by Direct Type I and Type II Profiler 78

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v LIST OF FIGURES Figure 2-1 Rod and Level 13 Figure 2-2 FACE Dipstick 14 Figure 2-3 APL Profilometer 16 Figure 2-4 BPR Roughometer 18 Figure 2-5 Lightweight Profiler and Non-contact Sensor 20 Figure 2-6 ICC Laser Profiler 21 Figure 2-7 ARAN Laser Profiler 21 Figure 3-1 Calculation Method of Direct Type I System 27 Figure 3-2 Calculation Method of Direct Type II System 28 Figure 3-3 Quarter-car Model 30 Figure 3-4 IRI Roughness Scale 35 Figure 3-5 Sensitive Wave Number of IRI 36 Figure 3-6 Subjective Rating Scales for Roads 37 Figure 3-7 Sensitive of RN to Wave Number 39 Figure 4-1 Direct Type I Walking Profiler 46 Figure 4-2 Direct Type II Walking Profiler 46 Figure 4-3 System Diagram of Direct Type I Profiler 47 Figure 4-4 System Diagram of Direct Type II Profiler 49

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v Figure 4-5 DAPRES Main Interface Form 51 Figure 4-6 Data Sampling Form 52 Figure 4-7 System Configuration Form 53 Figure 4-8 Profile Data Review Form 54 Figure 4-9 Analysis Results Summary Form 54 Figure 4-10 Subsection IRI Value Form 55 Figure 4-11 Subsection RN Value Form 55 Figure 4-12 System Calibration Form 56 Figure 4-13 Draft Calibration Form 56 Figure 4-14 System Parameters Setup Form 57 Figure 5-1 Correlation between High-Speed Laser Profiler and Direct Type I Profiler (RN, 30mph, Rate 1) 63 Figure 5-2 Correlation between High-Speed Laser Profiler and Direct Type I Profiler (RN 45mph, Rate 1) 64 Figure 5-3 Correlation between High-Speed Laser Profiler and Direct Type I Profiler (RN, 60mph, Rate 1) 64 Figure 5-4 Correlation between High-Speed Laser Profiler and Direct Type I Profiler (RN, 30mph, Rate 2) 65 Figure 5-5 Correlation between High-Speed Laser Profiler and Direct Type I Profiler (RN, 45mph, Rate 2) 65 Figure 5-6 Correlation between High-Speed Laser Profiler and Direct Type I Profiler (RN, 60mph, Rate 2) 66 Figure 5-7 Correlation between High-Speed Laser Profiler and Direct Type I Profiler (RN, 30mph, Rate 3) 66 Figure 5-8 Correlation between High-Speed Laser Profiler and Direct Type I Profiler (RN, 45mph, Rate 3) 67

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vi Figure 5-9 Correlation between High-Speed Laser Profiler and Direct Type I Profiler (RN, 60mph, Rate 3) 67 Figure 5-10 Correlation between High-Speed Laser Profiler and Direct Type I Profiler (RN, 30mph, Rate 4) 68 Figure 5-11 Correlation between High-Speed Laser Profiler and Direct Type I Profiler (RN, 45mph, Rate 4) 68 Figure 5-12 Correlation between High-Speed Laser Profiler and Direct Type I Profiler (RN, 60mph, Rate 4) 69 Figure 5-13 IRI Correlation between FACE Dipstick and Direct Type I Profiler 70 Figure 5-14 IRI Correlation between Direct Type I Profiler and High-Speed Laser Profiler (30mph, Rate 1) 72 Figure 5-15 IRI Correlation between Direct Type I Profiler and High-Speed Laser Profiler (45mph, Rate 1) 73 Figure 5-16 IRI Correlation between Direct Type I Profiler and High-Speed Laser Profiler (60mph, Rate 1) 73 Figure 5-17 IRI Correlation between Direct Type I Profiler and High-Speed Laser Profiler (30mph, Rate 2) 74 Figure 5-18 IRI Correlation between Direct Type I Profiler and High-Speed Laser Profiler (45mph, Rate 2) 74 Figure 5-19 IRI Correlation between Direct Type I Profiler and High-Speed Laser Profiler (60mph, Rate 2) 75 Figure 5-20 IRI Correlation between Direct Type I Profiler and High-Speed Laser Profiler (30mph, Rate 3) 75 Figure 5-21 IRI Correlation between Direct Type I Profiler and High-Speed Laser Profiler (45mph, Rate 3) 76 Figure 5-22 IRI Correlation between Direct Type I Profiler and High-Speed Laser Profiler (60mph, Rate 3) 76 Figure 5-23 IRI Correlation between Direct Type I and Direct Type II Profiler 79 Figure 5-24 RN Correlation between Direct Type I and Direct Type II Profiler 79

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viii DEVELOPMENT OF A DIRECT TYPE ROAD ROUGHNESS EVALUATION SYSTEM Fengxuan Hu ABSTRACT Roughness is an important indicator of pave ment riding comfort and safety. It is a condition indicator that shoul d be carefully considered when evaluating primary pavements. At the same time, the use of r oughness measurements plays a critical role in the pavement management system. There are many devices used for roughness evaluation. The major tools used for road roughness quantify are the ro ad profilers. In the thesis re search, in order to obtain useful pavement surface condition data for pavement evaluation, two direct type road roughness evaluation systems were developed with the combin ation of modern sensor technology and computer technology. The thesis will focus on the development of the direct type profiler systems, including the improvement of the hardware design, the new direct type road roughnessmeasuring system using different method, th e software development, which makes it more functional. In order to evaluate the accuracy and corre ction of the direct type profiler system, different roughness devices (including FDOT High-Speed laser profiler, FACE Dipstick

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ix and direct type I profiler) were operated in 4 calibration sites. The research focused on several performance measures, such as corre lativity, repeatabilit y. IRI and RN results from these devices were analyzed to evalua te the correlativity between these devices. After verified that direct type I profiler has good repeatability and correlation with FDOT High-Speed laser profiler, FACE Dipstick, 10 calibration sites data in Tampa were collected using direct type I profiler and di rect type II profiler. The repeatability and correlation analysis between the two profilers were performed. From field experiments and data analysis, it shows: 1. Direct type I profiler showed satis factory repeatability performances; 2. Direct type I profiler has good RN correlations Hi gh-Speed laser profiler; 3. Direct type I profiler has good correla tions with Dipstick, High-Speed laser profiler in terms of IRI 4. Direct type II profiler do es not has good correlation with direct type I profiler; the performance needs to be improved. Except for these conclusions, it is also f ound that the High-Speed profiler can be operated at different speeds with little differences in RN values, the sampling rate did show impact on RN value.

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1 CHAPTER 1 INTRODUCTON 1.1 Research Background Pavement roughness is one of the most important performance measures for pavement surface performance conditions. Pa vement roughness is also an important indicator of pavement riding comfort and safety. Roughness condition has been used as the criteria for accepting new construction of pavement (including overlay) and also as the performance measure to quantify the surf ace performance of existing pavements in a pavement management system at both networ k level and project level. For example, roughness can be used for dividing the network into uniform sections, establishing value limits for acceptable pavement condition, and setting maintenance and rehabilitation (M&R) priorities, or roughness measurements are used to locate areas of critical roughness and to maintain construction quality control. The need to measure roughness has brought a wide of instruments on the market, covering range from rather simple devices to quite complicated systems. In the past decades, roughness measurement instruments had become the everyday tools to measure road roughness. A substantial body of knowledge exists for the field of system design and technology. There are also many proven met hods for analyzing and interpreting data similar to the measurement results obtained from these systems.

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2 By far, the major tools app lied in the road roughness quant ify is the road profilers. A variety of devices are available today to measure a road profile. These devices range from the hand-held Dipstick™ profilers, high-speed, vehicle-ba sed profilers and Response-Type Systems. The former devices are based on mathematical modeling of the measured pavement surface profiles so the re sult indices are repeatable. However, the latter systems that were also called as road meters are always a passenger car, a van, a light truck, or a special trailer. Engineers install devices to record suspension stroke as a measure of roughness, normally it is a transd ucer that accumulates suspension motions and is known as response-type road roughne ss measuring system (RTRRMS). Response type indices are vehicle dependent and are not repeatable -even when the same vehicle is used -due to changes in the vehicle's ch aracteristics over time and driver’s driving behavior. At the same time, difficulties exist in the correlation and transferability of measures from various instruments and the cal ibration to a common scale, a situation that is exacerbated through a large number of factors that cause variations between readings of similar instruments, and even for the sa me instruments. The n eed of correlation and calibration led to the advent of the Intern ational Road Roughness Experiment (IRRE) in Brazil in 1982, which was also led to pub lish of International Roughness Index (IRI). The research leading to the development of roughness measuring equipment dates back more than 60 years. Early profilers we re time and labor consuming, required testing at very slow speeds. With the help of the development of se nsors technology and computer technology, it is no longer the case nowada ys. In the thesis research, in order to obtain useful pavement surface condition data for pavement evaluation, two measuring

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3 systems was developed with the combination of the modern sensor s and computer. The first profile roughness measuring system us es the absolute tilt angle and includes measuring the profile, filtering the profile to get only those waves of interest, and mathematically computing all major types of roughness index. The second one uses the relative angle to measure the profile. The repeatability and correlation analysis between the two profiles were introduced. They are both Direct Type road roughness evaluation systems. 1.2 Basic Concepts 1.2.1 Profiles The evaluation of the entire pavement su rface is required to define roughness completely. However, for most purposes, r oughness can be divided into three profile components of distortion: transverse, longitudi nal, and horizontal. Of particular interest are variations in profile that impart accele ration to the vehicle or occupant and thus influence comfort and safety. Here, we will focus on longitudinal profiles. Distortions of the pavement surface can generate both vertical and lateral acceleration in the vehicle. Vertical acceler ation is the major contributing factor to occupant comfort and derives from longit udinal distortion of the pavement profile. Lateral accelerations are the re sult of vehicle roll and yaw. Roll results from rotation about the longitudinal axis of the vehicle while yaw is the ro tation about the vertical axis. The curvature of the roadway, which contribut es to yaw, is normally handled through

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4 good geometric design. Roll results from diffe rential transverse pa vement elevations. Under severe conditions, it can impart an undesirable level of ve rtical acceleration. It is possible to take many profiles for a road along a different line. However since approximately 70 percent of vehicles travel in a well-defined wheel path with the right wheel located 2.5 to 3.5 feet from the paveme nt edge, the wheel tracks of automobiles and trucks are approximately 6 and 7 feet apart, respectively. Therefore, line measurement of the longitudinal profile on th e wheel path provides the best sample of road surface roughness. Furt hermore, comparison between the two wheel paths can provide some measure of the transver se variations that affect roll. Based on the pavement roughness definition, it is concluded that road roughness evaluation requires measurement of the longi tudinal profile of the pavement in the vehicle wheel path. The profile of a roa d, pavement, or ground can be measured along any continuous imaginary line on the surface and in order to obtain repeatable measures. It helps to make the line phys ically by using paint. For e ngineering interpretation, the measurements are usually handled with math ematical model that generates a summary statistics, ranged from power spectru m to some type of roughness index. 1.2.2 Profile Index A profile index is a summary number calculated fr om the data that make up a profile. The profile index is por table, reproducible and stable with time. Almost all road profiling system include two summary roughne ss statistic like, In ternational Roughness Index (IRI) and Ride Number (RN). There are other Roughness Indices in use; however, because they are not widely available in the fo rm of software and th ey correlate so highly with IRI, we will focus on the former two indices.

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5 1.2.3 Roughness Definition From an auto driver’s point of vi ew, pavement roughness is a phenomenon experienced by the passenger and operator of a vehicle. According to the definition (E867) of the American Society of Tes ting and Materials (A STM), “roughness is the deviations of a pavement surface from a tr ue planar surface with characteristic dimensions that affect vehicle dynamics, ri de quality, dynamic loads, and drainage, for example, longitudinal profile, transverse profil e, and cross slope”. This definition covers the factors that contribute to road roughness and it is also ve ry broad. However, it does not provide a quantita tive definition or standard scale for roughness, so it still requires a measurement and analysis method for quantif ying distortions of the pavement surface. Once the measurement and analysis method is selected, individual ag encies can establish interpretation scale to determine the severity of the roughness level. At the same time, pavement roughness consists of random multifrequency waves of many wavelength and amplitudes. Longitudinal roughness has been defi ned as "the longitudi nal deviations of a pavement surface from a true planar surface w ith characteristic dimensions that affect vehicle dynamics, ride quality and dynamic pavement load". Pavement profiles are frequently used to characterize roughness. There are several causes of pavement roughness: traffic loading, environmental effects, construction materials and built-in c onstruction irregularities. All pavements have irregularities built into the surface during construction, so even a new pavement that has not been opened to traffic can exhibit roughness. The rough ness of a pavement normally increases with exposure to traffic loadi ng and the environment. Short-wavelength roughness is normally caused by localized paveme nt distress, that is, depression and

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6 cracking, at the same time the long-wave length roughness is normally caused by environmental processes in combination with pavement layer properties. 1.2.3.1 International Roughness Index (IRI) The International Roughness Index (IR I) was established in 1986 by the World Bank and based on earlier research work perf ormed by NCHRP. It was first introduced in the International Road Roughness Experiment (IRRE) that was held in Brazil. IRI is calculated from a measured longitudinal road profile by accumulating the output from a quarter-car model or directly derived from a class 1 or class 2 instruments and divided by the profile length to yield a summary roughness index with units of slope. The IRI has been reported to be relevant as an indicat or of pavement serviceability, independent of the particular equipment used to measure roughness, it is internationally and geographically transferable and time stable. IR I is often used as an accepted standard against which roughness measuring systems are calibrated. 1.2.3.2 Ride Number (RN) Ride Number is a profile index intended to indicate ride ability on a scale similar to PSI. The longitudinal profile measurements taken with a profiler are processed using a computer program to obtain the RN, which matches the mean panel rating of a rating panel. Rider Number is an estimate of Mean Pane l Rating and uses the 0 to 5 scale. It is a nonlinear transform of PI. It is ideally calculated from the profiles in the left and right wheel paths of automobiles. The method was to be provided as porta ble software similar to that available for the IRI. Details of Ri de Number are handled in computer software.

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7 1.2.4 Signal Processing and Filter Modern profilers produce sequence of numb ers called as a signal. The outputs of the transducers in the prof ile are converted to numbers and processed by computer. Signal processing is the mathematical an alysis and transformation of signals. There are mainly two reasons for the signal processing: the first is to improve the quality of a measurement by eliminating unwanted “noi se” from the data, and the second is to extract information of interest from the signal. A profile can be considered consists of different wavelengths, varying from a few inches to hundreds of feet. To analyze a profile for roughness, it is important that the profile be filtered to include only those waves of interest. 1.3 Research Objective During the study period and based on previous work, two multifunctional pavement evaluations and survey instrume nts were developed, both are Direct Type Automatic Pavement Evaluation System (DAPRE S) – one uses absolute tilt angle of the road and the other uses relative angle to get pavement profile. The direct type I walking profiler relies on the combination of a tilt sensor and a distance sensor housed together within a surv ey cart. It can be classified as class 1 instrument for pavement roughness measuremen t that is operated at the walking speed. The roughness output of the system consists of the raw pavement profile, filtered pavement profile of those waves of interest and the major pavement indices like IRI value and RN value.

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8 However, there are problems exist in the direct type I profiler system. First, the operator has to push direct type I profiler at a smooth and almost constant speed during data collection. Also there are angle compensations at the begi nning and the end of data collection. The angle compensation is constant at different horizontal acceleration. The last problem is that the results get from the direct type I profiler are affected if the operator changes speed during data collection In order to solve these problems, direct type II profiler was developed. It employs a tilt sensor, which is used to get the first til t angle of the pushing cart, a rotary sensor, which was used to get the relative angle of th e front part to the back part in the pushing cart and a distance sensor to get distance information. Afte r developed direct type II profiler system, the system performance need s to be verified; the repeatability and correlation between direct type I and direct type II profiler ar e also need to be tested. The thesis will focus on the development of these systems, including the improvement of the hardware design in or der to make it rigorous and reliable, the software development, which makes it function al. At present, the accompanying software can accomplish all major profile measuremen t function from the minimization of the measurement error, filter t echnology and the on board calcula tion of the major pavement condition indices. In order to test the system performa nce and repeatability, correlation between direct type I profiler and Dipstick, laser pr ofiler, the ordinary fi eld-testing and data collection work for the development of th e systems were made through the research period. Based on the recommendati on by FDOT state materials office, four test sites in Gainesville area were selected for the resear ch. The filed data collection was performed

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9 by direct type I profiler, Dips tick and laser profiler. For the repeatability and correlation of our two profile systems, the systems were run along the same marked wheel paths of several testing sections in Tampa area. In order to make the correlation model more useful at different operation speed, the roughness level of the test sect ions covers a wide range of surface roughness. 1.4 Outline of the Thesis This thesis consists of six chapters. Ch apter 1 is a comprehensive introduction of the thesis. Chapter 2 focuses on literature review to describe past st udies on rela ted topics and some road roughness systems are also introduced. Chapter 3 will summarizes the methodology of the whole thesis including the methods used in the two direct type profiler system, the general algorithm for the IR I calculation, RN calculation methods, and digital filter’s design. Chapter 4 consists of the detailed description of the system development including hardware design and so ftware user interface and all the functions. Chapter 5 is a major part of the thesis. It includes the intr oduction of the data collection, the data analysis models. Some results will also be shown in this chapter. Chapter 6 presents the summary, final conclusi on and recommendation of this study.

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10 CHAPTER 2 LITERATURE REVIEW In order to understand the pavement roughness and pavement roughness measurement problems, current roughness meas uring system situation, an overview of the past studies and the research about di fferent pavement measurement systems are introduced in this chapter. The significance of related topics and th e potential study topics expected are also presented. 2.1 Overview A lot of studies and research have been done on the subject of pavement roughness since 1960s. In the late 1960s, afte r Spangler and Kelley developed GMR profilometer at the General Motors Resear ch Laboratory, the routine analysis of pavement profiles began. NCHRP sponsor ed a study of response-type road roughness measuring system such as the BPR roughometer and vehicles equippe d with Mays rider meters in the late 1970s. An objective of the study was to develop calibration methods for the response type systems. The best correla tion was obtained by using the Golden Car. In the late 1970s, when many state and federal ag encies in charge of monitoring pavement conditions began using profilers to judge the se rviceability of roads, profiling technology found broad application beyond research in the United States. A major advantage of

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10 profilers is that they are capab le of providing a stable and transportable way of measuring roughness. In other words, roughness valu es produced by a valid profiler can be compared to values from prior years and values measured by other valid profilers. Unfortunately, insufficiencies in profile r design, data processing techniques, and operational practices have compromised the accuracy of profile measurement. In 1982 the World Bank initiated a correlatio n experiment to establish correlation and the calibration standard for road roughness measurement and to develop IRI became an objective of the research program. The main criteria were that IRI is relevant, transportable and stable with time. The Golden Car simulation was one of the candidate references considered. After pr ocessed the data, a quarter ca r and a half car model were found with two vehicle simulations based on Golden car parameters. The quarter car model was selected because it could be used wi th all profiling methods that were in use at that time. Then “Guideline for conducting and calibrating roughness measurements” was published in 1986. This technical paper presente d the instructions for using various types of equipment to measure profile and get IR I. It also included computer code for calculating IRI of pavement profile. In 1990, the United States FHWA required IRI as the standard reference. In the 1990s, the ultrasonic sensors were used in response type road roughness measuring system. The ultrasonic profiler is a faster and reliable system. But it has problems with the response type systems: excep t that ultrasonic sens ors were found to be insufficient for measurement of IRI and RN the measurement became erroneous in the presence of water; the system was very sensitive to the pavement texture and it needs a lot of maintenance. Due to slow response time of ultrasonic sensor s, the test speed of

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11 ultrasonic profile was slow. Through the development and improvement of laser technology, laser sensors were used in that la te 1990s, the measuring speed of the profiler was improved. The measuring speed could be up to 60 miles per hour. Nowadays, many state department of transportation in the United States use the laser profiler to measure the pavement roughness. 2.2 Roughness Measurement Systems Pavement profile may be measured in th e field and evaluated or summarized by computer, or it can be processed through a m echanical response type device. The need to evaluate roughness of pavements was recogn ized in the 1920s. th e concept of the functional performance of pavements was devel oped at the AASHO Road Test in the late 1950s. The most straightforward techniques for measuring the profile of a pavement is with precision rod and level survey. However, it is time consuming, costly and limited to the evaluation of short length of pavements. So there are many kinds of pavement roughness measuring system in the United St ates. Generally, the roughness measuring system can be divided into three classes: 1. Class I. Manually operated instruments accurately measure short wavelength profiles of the pavements. The measurement interval is less than equal to 1 foot, and the maximum error is 1.5 percent bias, or 19 inches/mile. 2. Class II: Dynamic direct pr ofiling instruments that employ a variety of methods to produce elevation data. The measurement interv al is less than or equal to 2 feet, and the maximum error is 5 percent bias, or 44 inches/mile.

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12 3. Class III: Response Type Road Roughness Measurement System (RTRRMS), which accumulates suspension deflections from the roadway surfaces. The maximum error associated with the operati on of these instruments is 10 percent, or 32 to 63 inches per mile. Class I and class II include instruments used in the measurement of the shorter wavelengths contained in the pavement surf ace profiles. The instruments within these classifications possess the highest resolution and the smallest acceptable maximum error. The disadvantages of class I a nd class II devices are the lo w operating speed and the need to close the facility while the measurements are performed. 2.2.1 Class I System 2.2.1.1 Rod and Level Rod and level (shown in Figure 2-1) is cal led static because the instruments are not moving when the elevation measures are taken. It is conventional surveying equipment consisting of a precision rod, a leve l for establishing the horizontal datum, and a tape to mark the longitudinal di stance for elevation measurement. Figure 2-1 Rod and Level

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13 2.2.1.2 Dipstick The Dipstick (shown in Figure 2-2) is a device developed, patented, and sold by the Face Company. It is the simplest devices fo r measuring the profile of the pavement. It consists of an inclinometer mounted on a frame; a handle and a microcomputer are mounted on the Dipstick. The Dipstick is “walked” along the line being profiled. The distance between the two support feet are 305 mm apart. To get the profile along the ground, the surveyor leans the device so all of its weight is on th e leading foot, then raising the rear foot slightly off the ground. Then you pivot the device 180 degree about the leading foot, locating the other foot (for merly behind) in front, along the line being profiled. The computer monitors the sensor continuously. When it senses th e instrument has stabilized, it automatically records the change in elevat ion and beeps, signaling that the next step can be taken. Figure 2-2 FACE Dipstick

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14 The reference elevation is the value calculated for the previous point. The height relative to reference is deduced by the angle of the device relative to gravity, together with the spacing between its supports. The longitudinal distance is determined by multiplying the number of measures made with the known spacing. Data analysis for IRI computations is computerized and a continuou s scaled plot of surface profile can be printed. However, the Dipstick does not have the capability to generate RN measurements. 2.2.2 Class II System 2.2.2.1. K.J. Law Profilometer This profiler is a refined version of the original GM-type inertial profiler. The original GM profiler was deve loped in the 1960s using inertial reference concept. The original model consisted of two spring-lo aded, road-following wheels mounted on arms beneath the vehicle. These arms were held in contact with the road by 300-lb spring force. A linear potentiometer measured th e relative displacement between the road surface and a computed inertial reference. Vehicle frame motion is measured by doubling integration of the signal from accelerometer s, which are mounted on the frame over each of the rear wheels. These accelerometers se nse the vertical motions of the vehicle body relative to an inertial reference. Frame mo tion is added to the relative displacement motion. Two profiles result one for the right and one for the left wheel path. Improvements to the original profiler desi gn by K. J. Law Engineers Inc., include "the conversion to a digital instrumentati on system, a non-contacting road sensor, and a digital, spatial-based processing method for computing the measured profile. The

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15 processing method produces profile measurem ent that are independent of measuring speed and changes in speed during measurement." Profiles are measured in real time by a non-contacting optical displacement measuri ng system and precision and accelerometers in the right and left wheel paths. The acce lerometers measure vehicle motion while the optical measuring system measures displace ment between the vehi cle body and the paved surface. These two inputs are fed into the system's on-board microcomputer, which computes the road profile. 2.2.2.2. APL Profilometer The Longitudinal Profile Analyzer (APL, shown in Figure 2-3) was developed by the French Road Research Laboratory. It consis ts of a towed trailer with a combination of instrumentation and build-in mechanical proper ties that allow longitudinal profile to be measured. The profile reference is provide d by an inertial pendulum instead of an accelerometer. This pendulum is centered by a coil spring and amped magnetically. A low voltage displacement transducer is locat ed between the pendulum and the arm of the road wheel. Figure 2-3 APL Profilometer

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16 As the trailer wheel moves up and down in response to the road roughness, the angle between pendulum and wheel frame is measured and converted to a vertical distance measurement, which is recorded at specified distance intervals. Due to the mechanical nature of the device, measurements must be performed at constant speed; the response is quite sensitive to the speed. Meas urement of the profile distortions that are significant for highway pavements requires op erating the APL at approximately 13 mph. 2.2.2.3 South Dakota Profiler The South Dakota Profiler was develope d by the South Dakota Department of Transportation in 1981. It is typically mounted in a small to mid-sized van and measures pavement profile and rut depth. Mounted on th e front of the initial vehicles are an accelerometer and ultrasonic sensor for profile measurement in on wheel path and three ultrasonic sensors for the measurement of th e rut depth. Profile elevation measurements are reported at 1 feet interval and rut depth elevations are m easured and reported at 2 feet intervals. Testing speed can range up to 65 mph. Roughness output has been re ported by South Dakota profiling system by a PSI value computed form the measured profile data. Profile data are processed nearly instantaneously by the system software usin g correlations between measured profile values and rating panel values from surveys conducted in South Dakot a. It also has the capability to generate IRI fr om measured profile data. 2.2.3 Class III System There are two basic designs of respons e-type road roughness measuring systems or devices: these measuring the displacem ent between the vehicle body and axle, and

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17 those that use accelerometer to measure the response of the vehicle axle or body. In reality, these devices measures the response of the vehicle to the roughness of the road; hence, the term RTRRMS to describe this cla ss of measuring equipment. Due to their low cost, simple design, and high operating speed, th ese devices have been widely used by highway agencies to collect roughness da ta for pavement management system. 2.2.3.1 BPR Roughometer The BPR roughometer (shown in Figure 2-4) was first intro duced in 1925, and was recognized as being the best high-speed roughness-measuring device available at that time. It consisted of a single wheeled trailer that is towed by a car or a light truck at a speed of 20 mph. The wheel is mounted on le af springs supported by the trailer frame. Pavement surface contours cause the sensing wheel to oscillate vertically with respect to the frame. The vertical movement is accumula ted using a numerical integrator, yielding a roughness statistic in terms of in/mile. Figure 2-4 BPR Roughometer

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18 After some period of use, it was learned that the equipment was highly susceptible to changes in temperature and to the condition of its bearings and other mechanical components. In addition, it has a resonant frequency problem that, it excited, produced erroneous results. Vibrations were commonly noted at high roughness levels. As a result, its use has gradually declined. 2.2.3.2 Light Weight Profiler The lightweight non-contact profiler (s how in Figure 2-5) has emerged for pavement quality control and pavement evaluation purposes. It provides the benefit of use immediately after hot-mix asphalt cons truction and much sooner than would be possible with the network level devices on ne w pavements. However, they have operating speeds ranging from 8 to 25 miles per hour, wh ich makes it impractical for high speed, large road network data collection. The basic system consists of an accelerometer, a non-contact sensor distance measuring instrument, a graphic display, a no tebook computer, with a graphics printer. Inputs from the accelerometer and non-contact sensor are fe d to the system's on-board computer, which calculates and stores a user selected smoothness index, and capable of storing as much as 13,000 miles of data. Pave ment profile data point s, taken every inch, are averaged over a running 12-inch interval a nd stored as profile points every 6 inches, or every inch if required. The results can be viewed on-screen or ou tput to the printer. The longitudinal measurements are independent of variations in vehicle weight, speed, extremes in temperature, sunlight, wind, and pavement color or texture. They can also calculate different smoothness indices using th e same data. The system also generates a

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19 profile graph plot with defect locations and must grind lines, which tells the user where the roughness exists and what corrective action to take. Figure 2-5 Lightweight Profile r and Non-contact Sensor 2.2.3.3 Laser Profiler The Laser Profiler uses an infrared laser and precision accelerometer to obtain an accurate, precise profile measur ement at speeds up to 65 MPH. It uses the measurement to calculate a profile index (PI), international roughness in dex (IRI), and ride number (RN), which is used to rate the surface smoothness. The system also generates a profilograph-type plot with de fect locations and must grin d lines, which tells the user where the roughness exists and what corrective action to ta ke. There are many companies that produce laser profilers, like Internatio nal Cybernetics Corporation (ICC), Roadware Group Inc. and etc. Figure 2-6 and Figure 2-7 are the pictures of ICC laser profiler and Automatic Road Analyzer (ARAN) of Roadware Group Inc. respectively. The laser profiler consists of industrial PC with printe r, precision accelerometer, laser height sensor, data ac quisition sub-system and distan ce measuring instrument. The axle-mounted accelerometer is not as sensitive to the vehicle parameters as the

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20 displacement type devices. Movement of th e axle in response to road roughness depends on the amount of tire distortion and the upward ve rtical force generated when the tire hits Figure 2-6 ICC Laser Profier Figure 2-7 ARAN Laser Profiler a bump and the downward vertical force of the vehicle suspension. If the force of the suspension on the axle is greater than th e upward force generated by the bump, then the tire maintains contact with the pavement so the axle provides a reasonable tracking of the pavement surface. The output of the acceleromet er can be integrated twice to obtain an estimate of the vertical axle movement. Howe ver, this integration process can magnify

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21 the effect of undesired noise in the signal. Generally the axle mounted RTRRMS’s use a measure of the root-mean-square acceleration of the axle to quantify pavement roughness. The data collected is not affected by ve hicle variation such as speed, weight and suspension. Measurements are not affect ed by changes in temperature, pavement color or texture, sunlight, wind and speed. The Profiler offers many benefits over th e conventional method of measurement. It doesn’t require any set up or break down and operates at speeds up to 65 MPH. This permits rapid, real-time measurements. This also eliminates the need for lane closures or traffic control to test existing pavements. When the Profiler is used on an all terrain vehicle it is so lightweight it can test pavements before they have completely set up. The Profiler can be provided on a ny vehicle required by the user The equipment is mounted to an all terrain vehicle or can be supplied to mount into any specified vehicle. The system collects data in realtime as it traverses the pavement’s surface. The raw data is processed and the results are output in standard or metric units on the flat panel display or graphics printer and are saved on a hard drive or floppy drive. The software of Laser profiler incl udes digital band-pa ss filters passing wavelengths of 1 feet to 300 feet, digita l high-pass filters passing filters passing wavelengths of 2 feet or less, and statisti cal models generating the reported roughness statistics root mean square vertical acce leration (RMSVA), mean absolute slope. The laser profiler provides surface pr ofile, IRI, Serviceability Index (SI) and Ride Number output.

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22 2.3 Roughness Indices A profile measurement is a series of nu mbers representing elevation relative to some reference. There could be thousands of numbers per mile of measured profile. A profile index is a summary nu mber calculated from the many numbers that make up a profile. At the same time, a profile inde x should have following characteristics: 1. Portable: It can be measured by different types of profiler inst rument, so long as they are valid for that index, 2. Stable with time: Because the concept of a true profile has the same meaning from year to year, it follows that a mathemati cal transformation of the true profile is also stable with time. In 1982 the World Bank initiated a correla tion experiment in Brazil called the International Road Roughness Experiment (IRRE) to establish correlation and a calibration standard for roughness measurement (Sayers 1991). In processing the data, it became clear that nearly all roughness-measur ing instruments in use through the world were capable of producing measures on the same scale, if that scale had been selected suitable. Accordingly, an objective was added to the research program: to develop IRI. The main criteria in designing the IRI were that it be relevant, tran sportable, and stable with time. To ensure transportability, it had to be measured with a wide range of equipment, including response-type systems. To be stable with time, it had to be defined as a mathematical transform of a measured profile. Many roughness definitions were applied to the large am ount of test data obtained in th e IRRE. The Golden Car simulation from the NCHRP project was one of the ca ndidate references considered, under the

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23 condition that a standard simulation speed w ould be needed to use it for IRI. After processing the IRRE data, the best correlati on between a profile index and the responsetype systems were found with two vehicle simulations based on the Golden Car parameters: a quarter-car and a half-car. Bo th gave essentially the same level of correlation. The quarter-car was selected for the IRI because it could be used with all profiling methods that were in use at that time. The consensus of the researchers and participants is that the standard speed s hould be 80 km/hr (49.7 mph) because at that simulated speed, the IRI is sensitive to the same profile wavelengths that cause vehicle vibrations in normal highway use. The resear ch findings were highly encouraging and led the World Bank to publish guidelines for conducting and calibrating roughness measurements. The researchers (Sayers, Gillespie, Queiroz and Paterson) prepared instructions for using various types of equi pment to measure IRI. The guidelines also include computer code for calc ulating IRI from profile. A co mpanion report described the IRRE, using many analytical comparisons of al gorithms and some sensitivity analyses. In 1990 FHWA required the IRI as the standard reference for reporting roughness in the Highway Performance Monitoring System (HPMS). The IRI is a general pavement conditi on indicator. It summarizes the roughness qualities that impact vehicle response, and is most appropriate when a roughness measure is desired that relates to: overall vehicl e operating cost, overal l ride quality, dynamic wheel loads (that is, damage to the road fr om heavy trucks and braking and cornering safety limits available to passenger cars) and overall surface condition. There are following properties of the IRI:

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24 1. It showed maximum correlation with the RTRRMSs in use, 2. It describes profile roughness that causes vehicle vibrations, 3. It is linearly propor tional to roughness, 4. It is the first highly portable roughne ss index that is stable with time. For decades, highway engineers have been interested in estimating the opinion of the traveling public of the roughness of roads. The PSI scale from the AASHO Road Test has been of interest to engi neers since its introduction in the 1950’s. Ride Number is a profile index intended to indicate ri de-ability on a scale similar to PSI. Direct collection of subjective opinion of Mean Panel Rating is too expensive and provides no continuity from year to year. Th e NCHRP sponsored two research projects in the 1980’s that investigated th e effect of road surface roughness on ride comfort. During two projects, mean panel ratings were determin ed experimentally on a 0 to 5 scale for test sites. The researchers investigated a quart er car analysis and found significantly less correlation between the quarter car index and panel rating than between a profile index based on short wavelengths. The profile-based analyses were deve loped to predict MPR. A method was developed in which PSD functions were cal culated for two longitudinal profiles and reduced to summary statistic called PI (profile index). The PI values for the two profiles were then combined in a nonlinear transfor m to obtain an estimate of MPR. There are following properties for the Ride Number analysis: 1. It uses the 0 to 5 PSI scale, 2. It is a nonlinear transform of a statistic called PI, 3. Ride Number is correlated to IRI but the two are not interchangeable.

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26 CHAPTER 3 METHODOLOGY 3.1 Introduction In the thesis research, two automatic pavement roughness-measuring systems were developed; both of them are automatic true road profil ers. But the me thods used in these two systems to get pavement profile are different: one uses the absolute tilt angle of the road, while the other uses the accumu lated rotary angle to get the profile. The methodologies used to reach the various ob jectives are presente d in this chapter. This chapter consists of th ree sections. The first secti on will explai n the methods used to get the pavement profile. Then tw o different models and algorithms measuring and calculating International Roughness I ndex (IRI) and Ride Number (RN) are introduced. Finally the digital filter will be explained and the discussion of the digital filter application in the pavement measurement system. 3.2 Methods to Get Road Profiles 3.2.1 Method Used In Direct Type-I Profiler The calculation method of the direct-t ype system is shown in Figure 3.1.

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26 The distance between the two wheels in this project is the sample interval. When the wheels move one round, the t ilt sensor will take a sample Since the circumference of the wheel and the distance between the two wh eels are the same, the front wheel at the time of (k-1) sample and the back wheel at the time of k sample should be overlapped. Figure 3-1 Calculation Method of the Direct Type-I System Assume: 1. L: the total length of measured pavement (m), 2. D: the distance between the two axles center of the pushcart (m), 3. Yk: the kth measured pavement elevation between the two axle s of pushcart (m), 4. k: the k th measured tilt degree of pushcart According to Figure 3.1, Yk can be calculated by the following equation: k k kSin D Y Y *1 The elevation of the first sample point is assumed to be zero, thus 00 Y Therefore, N k k kSin D Y1*

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27 Where, k: from 0 to N, the total sampled data numbers, N = L/D 3.2.2 Method Used In Direct Type-II Profiler The method used in direct type-II pr ofiler is depicted in figure 3.2. Figure 3-2 Calculation Method of Direct Type-II System The distance between the two wheels is the sample interval. First, the pushing cart is in static position, so the first angle 1 can be get through the tilt sensor, which is accurate in static condition; and n can be acquired using the rotary angle sensor, which is also accurate either in static condition or in dynamic condition. When the wheels move on round after the first sample, only the rotary sensor will take a sample, which is not sensitive to horizontal acceler ation. Since the circumferenc e of all three wheels and the distance between the three wheels are the sa me, the front wheel at the time of (k-1) sample and the back wheel at the time of k sample should be overlapped. Assume: 1. L: the total length of measured pavement (m) 2. D: the distance between the two adjace nt axles center of the pushcart. Y1 Y2 1 Y ( n-1 ) Y ( n ) 1 2 2 n-1 n

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28 3. Yn: the nth measured pavement elevati on between the two ax les of pushcart. 4. n : the nth measured rotary degree of two board in the push cart. In Figure 3.2, 1 is measured using tilt angle sensor in static condition, n is measured using rotary angle sensor. From this fi gure, we can get the following equation: 1 1 n n n Here, if the second board is on the top of the extended line, the symbol of n is positive, if the second board is on the lower of the extended line, the symbol of n is negative. So 2 ) ( ) ( *1 1 1 1 1 n Sin D Y Yn Sin D Yn n n n n where: n: from 1 to N, the total sampled data numbers, N=L/D 3.3 IRI and RN Models and Algorithms The International Roughness Index (IRI) ha s been widely used in many pavement roughness-measuring systems. IRI was first introduced in the In ternational Road Roughness Experiment (IRRE) that was held in Brazil. In addition to evaluation of pavement roughness performance, IRI is ofte n used as an accepted standard against which roughness measuring systems are calibrate d. The quarter-car model was used in the IRI algorithms. Sayers gives additional background on the IRI along with theoretical and practical issues with its measuremen t in Transportation Research Record 1501.

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29 Ride Number (RN) is the result of a NC HRP research in the 1980’s. RN is an estimate of Mean Panel Rating. Ratings fr om people reflect their opinions and are subjective. Subjective rating sc ales for road usually range from 0 to 5. When a group of ratings are taken together, the average rating can be fairly consistent. After statistical processing, the results are pr ocessed to yield a single ratin g for the panel as a whole, typically called mean panel rating (MPR). 3.3.1 Quarter Car Model The concept of quarter-car simulation as a method for analyzing pavement profile data was originally an attempt to simulate the output of the BPR roughometer [Hegmon, 1992]. Subsequently, vehicle simulation studies at the University of Michigan demonstrated that full-car and half-car si mulation models do not provide an advantage over the quarter-car simulation with respect to the calibration of RTRRMS devices and are computationally much complicated. The parameters of the quarter-car that are shown in Figure 3-3: Figure 3-3 Quarter-car Model

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30 these parameters include the major dynamic effects that determine how roughness causes vibration in a road vehicle. The masses, springs, and dampers are defined by the following parameters: the sprung mass of the vehicle body; the su spension spring and damper (shock absorber) constants; the Unsp rung mass of the suspension, tire, and wheel; and the spring constant of the tire. Theore tical correctness woul d require a damper constant for the tire. However, practical application generally ignores this term. Mathematically, the behavior of a quarter-car can be described with two-second order equations: 0 ) (.. .. Z Z K Z M Z MU t U U S S and 0 ) ( ) (. .. U S S U S S S SZ Z K Z Z C Z M Where 1. Z = road profile elevation, 2. Zu = elevation of unsprung mass (axle), 3. Zs = elevation of sprung mass (body) 4. Kt = tire spring constant, 5. Ks = suspension spring constant, 6. Cs = shock absorber constant, 7. Mu = unsprung mass (axle), and 8. Ms = sprung mass. The double dot notation above the elevati on terms represents acceleration while the single dot repr esents velocity.

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31 To simplify the equations, the paramete rs are normalized by the sprung mass, Ms. The following values for the normalized para meters define the Golden Car data set: K1 = Kt / Ms = 653, K2 = Ks / Ms = 63.3 C = Cs / Ms = 6.0 M = Mu / Ms = 0.15 Since RTRRMS devices generally measur e the movement between the vehicle axle and body, simulation requi res calculation of the differe nce in elevation between the body and axle in response to the road profile and forward motion of the vehi cle. This is accomplished by integrating the difference in the velocities between the sprung and unsprung mass; producing the quart er-car statistic, QCS: dt Z Z C QCST O U S .1 The terms C represents either the total time required to traverse the section of road or the length of the section, L. If the time factor is used to normalize the quarter-car statistic, the calculation results in an aver age rectified velocity, while a distance base yields the average rectified slope. There are several acceptable numerical techniques for the solution of the equation. However, the linear nature of the equations permits an exact solution with the state transition matrix method. Historically, two sets of vehicle parameters have been used for computing quarter-car statistics for calibration of RT RRMS devices. A set representing the original BPR Roughometer trailer was used for severa l years, until research at the Highway

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32 Safety Research Institute (HRSI) produced an updated set of vehicle parameters. The World Bank recommends the HSRI vehicle parameters and has termed the quarter-car statistic computed as the inte rnational roughness index, IRI. Although the mathematical base for quarte r-car simulation is somewhat complex, computer programs are readily available fo r performing the calculation. Fortran source code can be found at: http://www.umtr i.umich.edu/erd/roughness/rr.html. 3.3.2 Calculation of IRI The calculation of the international roughness index (IRI) is accomplished by computing four variables as functions of th e measured profile. (T hese four variables simulate the dynamic response of a reference ve hicle, shown in Figure 3-4, traveling over the measured profile.) The equations for the four variables are solved for each measured elevation point, except for the first point. The average slope over the first 11m (0.5 sec at 80 km/h) is used for initializing the va riables by assigning th e following values: 1 / 11 0 11 / ) (' 4 2 1 3 1 dx a Z Z Y Y Z Za where Ya is the “a-th” profile elevation point that is a distance of 11m from the start of the profile, Y1 is the first point and dx is the sample interval. The following four-recursive equations ar e then solved for each elevation point, from 2 to n (n =number of elevation measurement): 1 4 3 2 1 114 13 12 11 Y P Z s Z s Z s Z s Z 2 4 3 2 1 224 23 22 21 Y P Z s Z s Z s Z s Z 3 4 3 2 1 334 33 32 31 Y P Z s Z s Z s Z s Z 4 4 3 2 1 444 43 42 41 Y P Z s Z s Z s Z s Z

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33 Where: slope dx Y Y Yi i / ) (1 4 1' j position previous from Z Zj j Sij and Pj are coefficients that are fixed for a given sample interval, dx, thus, the equations above are solved for each position al ong the wheel track. After they are solved for one position, equation above is used to reset the values of Z1', Z2', Z3' and Z4', for the next position. Also for each position, the re ctify slope (RS) of th e filtered profile is computed as: 1 3Z Z RSi The IRI statistic is the aver age of the RS variables over the length of the site. Thus, after the above equations have been solv ed for all points, the IRI is calculated as: n z i iRS n IRI ) 1 /( 1 The above procedure is valid for any sample interval between dx=0.25 m and dx=0.62 m (2.0ft). For shorter sample interv als, the additional step of smoothing the profile with a 0.25m moving average is r ecommended to better represent the way in which the tire of a vehicle envelops the ground. Then the IRI is calculated by solving the equations for each average point using coeffici ents in the equations appropriate for the smaller interval. The computed IRI will have units cons istent with those used for elevation measures and for the sample interval. For example, if elevation is measured as millimeters and dx is expressed in meter, then the IRI will have the preferred units:

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34 mm/m=m/km=slope*10. The coefficients used in the equations are calculated from the equations of motion that define a quarter-car model. In the general case, they are specific to the vehicle model parameter values, simulation speed, and the sample interval. The IRI summarizes the roughness qualities th at impact vehicle response, and is most appropriate when a roughness measure is desired that relates to: overall vehicle operating cost, overall ride quality, dynamic wheel loads, and overall surface condition. Figure 3-4 shows IRI ranges repr esented by different of road. Figure 3-4 IRI Roughness Scale IRI is influenced by wavelengths ranged from 1.2 to 30 meters. The wave number response of the IRI quarter-car filter is show n in Figure 3-5. The amplitude of the output sinusoid is the amplitude of the input, mu ltiplied by the gain shown in Figure 3-5. The gain shown in the figure is dimensionless.

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35 The IRI filter has maximum sensitivity to slope sinusoids with numbers near 0.065 cycle/m (a wavelength of about 15m) and 0.42 cycle/m (a wavelength of about 2.4m.). The response is down to 0.5 for 0.03 and 0.8 cycle/m wave numbers that correspond to wavelengths of 30m and about 1. 25m, respectively. Ho wever there is still some response for wavelengths outside this range. An IRI of 0.0 means the profile is perfectly flat. There is no theoretical uppe r limit to roughness, although pavements with IRI values above 8 m/km are nearly impassable except at reduced speeds. Figure 3-5 Sensitive Wave Number of IRI For the specific case of IRI, defined by the NCHRP 228 parameters [Wambold, 1980] and a standard 80km/hr si mulation speed, they depend only on the sample interval. Complete instructions for measuring IRI are available in Sayers, gillespie, and Queiroz [Janoff et al, 1990]. The instruc tions include listings of comp uter programs that solve the equations of motion and also computer pr ograms that calculate the coefficients.

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363.3.3 Calculation of RN RN is the result of two NCHRP researches performed in the 1980’s by Janoff to investigate the effect of road surface roughne ss on ride comfort. Th e objective of these researches was to determine how features in road profiles were linked to subjective opinion about the road from members of the public. During two studies, spaced at about a 5-year interval, mean panel ratings (MPR) we re determined experimentally on a 0-to-5 scale for test sites in several states. The 0 to 5 scale as show n in Figure 3-6 was used for a large-scale road test conduc ted by AASHO in the 1950’s, in which roads were subjected to mixed traffic and researchers trac ked the condition of the pavement. Figure 3-6 Subjective Rating Scales for Roads Longitudinal profiles were obtained from leftand righ t-wheel tracks of the lanes that were rated. RN is an estimate of MP R. The mathematical pr ocedure developed to calculate RN is described in NCHRP Report 275, but not in complete detail. In 1995, some of the data from the two NCHRP projects performed and a panel study conducted in Minnesota were analyzed again in a pooled-fund study initiated by the Federal Highway Administration to develop and test a practical mathematical process for

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37 obtaining RN based on objective measurement, not subjective rating. The method was to be provided as portable software similar to th at available for the IRI, but for predicting MPR rather than IRI. RN is a nonlinear transform of a statistic ca lled profile index (PI). PI is calculated from one or two profiles. The profile is filtered with a moving average with a 250-mm (9.85-in) base length. The m oving average is a low-pass filte r that smoothes the profile. The computer program does not ap ply the filter unless the profile interval is shorter than 167mm (6.6 in). The profile is further filtered with band-pass filter. The filter uses the same equations as the quarter-car model in the IRI. However differe nt coefficients are used to obtain the sensitivity to wave numbe r shown in the last figure. The quarter-car parameters for the PI calculation are: K1 = Kt / Ms = 5120, K2 = Ks / Ms = 390, C = Cs / Ms = 17 M = Mu / Ms = 0.036 The filtered profiles are reduced to yield PI, which should have units of dimensionless slope (ft/ft, m/m, etc). Then, PI is transformed to RN. RN is defined as an exponential transform of PI according to the equation: ) ( 1605PIe RN If a single profile is being processed, PI is calculat ed directly. If two profiles for both the leftand right-wheel tracks are processed, PI valu es from the two wheel tracks are averaged with the following equation, then previous equation is applied.

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38 22 2 R LPI PI PI Figure 3-7 shows the sensitivity of RN to wave number. The maximum sensitivity of RN is for a wave number of 0.164 cycle/m (0.05 cycles/ft), which is a wavelength of about 6 meters (20 ft). The IRI has a great se nsitivity to a wavelength of 16 meters (wave number of 0.065 cycle/m). The figure shows that RN has a low se nsitivity to that wavelength and even lower sens itivity for longer wavelengths. Figure 3-7 Sensitive of RN to Wave Number 3.4 Digital Filter A digital filter is a calculation procedure that transforms a series of numbers (a signal) into a new series of numbers. Digital filters are us ed for two general purposes: (1) separation of signals that have been combined, and (2) restor ation of signals that have been distorted in some way.

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39 In order to make practical use of a profile measurement, it is necessary to filter the sequence of numbers that makes up the profile. Profiling information is used to evaluate the condition of pavements and to manage road networks. A profile consists of different wavelengths, varying from a few centimeters to hundreds of meters. It is also necessary to filter profile data to view different types of profile features, i.e., the profile be filtered to include only those waves of interest. When analyzing pavement pr ofile, it is desirable to remove the long wavelengths when the road trend is desired, to remove the short wavelengths if expected to get the pavement details. In summary, digital filter is the math ematical analysis and transformation of signals. Signals are filtered mainly for two reasons: 1. to improve the quality of measurem ent by eliminating unwanted “noise” 2. to extract information of interest from the signal. There are four basic filter types; Low-pa ss, high-pass, band-pass and band-stop. There are also two types of filters: Finite impulse response (FIR) and infinite impulse response (IIR). In general FIR filters can be designed to have exact linear phase and there is also great flexibility in shaping their ma gnitude response. In addition, FIR filters are inherently more stable and th e effects of quantization errors are less severe than IIR filters. Conversely, IIR filters require fewer co efficients than FIR filters for a sharp cutoff frequency response, and analogue filters can only be modeled using IIR filters. The method of digital filter design is bu ilt upon a more fundamental approach that is call Fourier series method. This method is based on the fact that the frequency response of a digital filter is periodic and is therefor e represented as a Fourier series. A desired target frequency response is selected and e xpanded as Fourier series. This expansion is

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40 truncated to a finite number of terms that are us ed as the filter coefficients or filter orders. The resulting filter has a frequency response that approximates the original desired target response. Digital filters can be implemented in two ways, by convolution (called finite impulse response or FIR) and by recursion ( called infinite impulse response or IIR). The general form of the digita l filter difference equation is: N i N i i ii n y b i n x a n y01) ( ) ( ) ( where y(n) is the current filter output, the y(n-i)’s are previous filter outputs, the x(n-i)’s are current or previous filter inputs, the ai’s are the filter’s feed forward coefficients corresponding to the zero s of the filter, the bi’s are the filter’s feedback coefficients corresponding to the poles of the filter, and N is the filter’s order. IIR filters have one or more nonzero feedback coefficients. That is, as a result of the feedback term, if the filter has one or more poles, once the filter has been excited with an impulse there is always an output. FIR filters have no non-zero feedback coe fficient. That is, the filter has only zeros, and once it has been excited with an impulse, the output is present for only a finite (N) number of computational cycles. The recursive filter is described by a difference equation given by: ... ] 2 [ ] 1 [ ... ] 2 [ ] 1 [ ] [ ] [2 1 2 1 0 n y b n y b n x a n x a n x a n y By using the z-transform, we can system ’s transfer function can be given by: ... 1 ... ] [3 3 2 2 1 1 3 3 2 2 1 1 0 z b z b z b z a z a z a a z H

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41 So the major task of filter design here is to calculate the value of all coefficients, and the major steps to get all the coeffi cients are described as follows: Step 1. Specify a desired frequency response ) ( dH including the magnitude and cutoff frequencies, for example, the cutoff frequencies for band pass filter are L and H Step 2. Specify the desired number of filter orders N. Step 3. Compute the filter coefficien ts h(n) for n=0, 1, 2,..., N-1 using 2)] sin( ) )[cos( ( 2 1 ) ( d m j m H n hd The coefficients h(n) for an ideal band pass filter are calculated as: 0 )] sin( ) [sin( 1 0 ) ( m m m m m n hL H L H where: even n N n odd n N n m 2 / 2 / ) 1 ( During the research period, the Chebyshe v filter was developed. The Chebyshev filter is a mathematical strategy for achieving a faster roll-off by allowing ripple in the frequency response. Digital filters that use this approach are called Chebyshev filters.

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43 CHAPTER 4 SYSTEM DEVELOPMENT There are primarily two types of equipment measure Road roughness in the United States: direct type profiler, and re sponse type road roughness measuring system. Ideally, the road profiling method gives accura te measurement of the pavement profile along a reference path and the response-type method can be operated at high speed and needs to be calibrated using Dipstick or dire ct type profiler. The results from response type system are acceptabl e if calibrated accurately. This chapter describes the two direct type profilers design. It demonstrates the decisions made in the design, th eir rationale and the particular details of the hardware and software design. It is felt that the overall system description is necessary and then the Profiler software, also called the Direct T ype Automatic Pavement Roughness Evaluation System (DAPRES) is described. 4.1 System Requirements To obtain information from a measured pr ofile, there are two basic requirements: 1. The system must be capable of sampli ng the relevant information present in the true profile.

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43 2. The computer software must exist to pr ocess the measured values to extract the desired information (such as a summary index). Because pavement roughness is defined over a pavement profile and the length of it is normally five hundred to six hundred feet the size of data needed to get the profile will consume a lot of memory due to the small sample interval. It is complicated if the user want to process the profile to get what he wants. With the development of electronic and computer technology, we can use the el ectronic device and computer system to provide the data logging and da ta analysis process easily. Howe ver, there are still several factors need to be taken into account: 1. How the computer connect to the electric part of devices, that is, how the computer get the signal of the electric part of the sensors. 2. Other factors to be considered are th e easy use of sampling under the testing environment and software compatibility. Based on the above pavement roughness measurement requirements, we followed following approaches: 1. The profiler consists a designed pushing cart, a laptop computer is connected with sensors through A/D card with serial port or parallel port interface and combined with a specific software. 2. The power supply for the sensor is dir ectly from the laptop through computer interface, there is no need to have any extern al power supply. Actually, the hardware part of the device consumes a little power so it will not affect the computer’s normal work.

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44 4.2 Direct Type Profiler Hardware Development A profiler is an instrument used to produ ce a series of numbers related in a welldefined way to a true profile. It measures th e components of true profile that are needed for a specific purpose. A profiler works by combining three ingredients: 1. a reference elevation, 2. a height relative to the reference, and 3. Longitudinal distance. 4.2.1 Direct Type Profiler Introduction The objective in the development of the walking profiler is to develop an inexpensive, lightweight, easily transporta ble device. The profiler can measure the roughness of a long distance section in about 2 or 3 hours. It is supposed to be a highly precise machine suitable for measuring su rface roughness within a class 1 profiler confidence applied on the new pavement c onstructions, reconstruction or overlay. The profiler is a precision in strument designed to facilitate the efficient collection and presentation of continuous paved surface information, including distance, profile, grade and International Roughness Index (IRI), Ride Number (RN) values. The profiler enables accurate recording of measurements for actual profile, grade and level for surfaces such as paved roads, footpaths, runways, building slabs and sporting surface. Figure 4-1 is a photo of the direct type I profiler. Figur e 4-2 is the photo of direct type II profiler. These compact and easy-to-u se devices are pushed over the surface to be

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45 surveyed. The on-board computer calculates and displays graphics and tables results. The system comes with a complete software pack age that provides all the data acquisition, data processing work. It can also output all major roughness indices. Figure 4-1 Direct Type I Walking Profiler Figure 4-2 Direct Type II Walking Profiler

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46 4.2.2 Direct Type I Profiler Hardware Figure 4-3 shows a schematic di agram of the hardware architecture of the profiler. When the operator collects the pavement roughness data, he/she can walk under the controlled walking pace. The sampled data come from two sources, one is from the distance sensor and the tilt sensor provides the angle data. All the data will be transmitted to the laptop computer by which also powers all sensors. The computer will process the data and output the process result. The provi ded laptop computer is with the necessary communication cable and power cable. Figure 4-3 System Diagram of Direct Type I Profiler The profiler consists of two main parts: 1. The compact and easy to use push cart. Both the distance sensor and tilt sensor are mounted on the internal side of the push cart, 2. The provided laptop computer and the necessary communication cable and power cable. Data Sampling Data Processing: IRI and RN System Configuration Tilt Sensor Distance Sensor A/D Converter Transducer RS232 Serial Port N otebook Com p ute r

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47 4.2.2.1 Sensors and Conversion Module The most important and the fundamental part of the profiler system is the sensors part. It contributes the most pa rt of measure errors to the system. There are a lot of kinds of sensors applied in the prof iler system before like the lase r sensors, ultrasonic sensors, accelerometers and attitude sensors. The laser sensors and ultrasonic sensors are expensive and require the high speed A/D converter. The accelerometers can’t give the roughness data directly and need to be integrated twice, which will incur noise erro r. In our system, the attitude sensor was applied to provide the tilt angle information. Th e distance sensor is trigged every 30cm in order to give the operation length information. The electrolytic tilt sensors have been us ed for many years and it is proved that it can provide a reliable way to measure tilt an gles in static and dynamic environments. The sensor, when connected to an appropriate electronic conversion module, provides an output proportional to a corre sponding inclination angle. There is a highly advanced and flexib le angle conversion modules accompany with the tilt sensor. It can pow er the sensor, provides concu rrent analog and continuous digital inclination information at the same time. 4.2.2.2 Interface The direct type I profiler uses the 12-b it A/D converter that provides the serial port interface to the computer. It can send the converted tilt angle data to computer and computer can send command to the A/D converter to get data and parameter setting. The profiler uses the other signal called “Data Rea dy” of the serial po rt to get distance information.

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48 4.2.3 Direct Type II Profiler Hardware Figure 4.4 show the system function of direct type II profiler. The system also has three basic functions: 1. Data Sampling, 2. Data Processing and 3. System configuration (Including cali bration and parameters set-up). The main differences of direct type I and direct type II systems are the method and the interface to the computer. Figure 4-4 System Diagram of Direct Type II Profiler There are three sensors to get data. One is from the tilt sensor, which gets the first tilt angle when the profiler is at the first position; the distance sensor gets the distance that the profiler passed and the rotary angle se nsor that get the angl e data corresponding to the last position. All the data will be tran smitted to laptop through parallel interface. The power supply for these sensor s is provided directly from the laptop’s power source, so there is no need to have any external power supply. Actually, the sensors consume Data Sampling Data Processing: IRI and RN System Confi g uration Tilt Senso r Distance Senso r A/D Converter Transducer Parallel Port Interface Notebook Computer Rotary Senso r

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49 only a little power source (20mA at 5Volts). Thus it will not affect the computer’s normal work. Generally, a fully charged computer battery is able to continuously work for more than 2 hours. The type II profiler has two major parts: 1. The pushing cart – The distance sensor, til t sensor and rotary angle sensor are mounted on the pushing cart. 2. The laptop and cables (including po wer cable and communication cable). 4.2.3.1 Sensors In direct type II profiler system, the tilt and rotary se nsor were applied to provide the tilt angle information. The distance sensor is trigged every 12.5cm in order to give the distance information that the profiler has passed. The rotary sensors are proven to provide a reliable way to measure angle in static and dynamic environments. The sensor, when connected to an appropriate electronic conversion module, provides an output proportional to a corresponding rotary angle between the two mechanic parts of system. 4.2.3.2 Interface The type II system used th e 12-bit A/D converter parall el port inte rface card to provide the digital tilt data, rotary angle data and communi cate with the laptop computer. The type II system uses the 12-bit A/D convert er through parallel in terface that is not expensive; also the sampling speed is fa ster the serial port interface A/D card.

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50 4.3 Profiler Software Development 4.3.1 Overview The software transforms the computer and data acquisition hardware into a complete data acquisition, analysis, and ca libration system. Its name is DAPRES (Direct Type Automatic Pavement Roughness Evalua tion System). DAPRES is developed in Visual Basic and runs on a personal computer under Window 9x, window 2000 and Windows XP operating systems. DAPRES has three main functions: (1) Data Sampling, (2) Data Processing and (3) System calibration and configuration. Th e user-friendly GUI takes the user through the software set-up features in different screens effortlessly with the context help providing useful instructions. Figure 4-5 is the main menu of the system. Figure 4-5 DAPRES Main Interface Form

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51 4.3.2 Data Sampling Module The Data Sampling Module executes in a real-time environment. It functions as the resource of the data acquisition and data storage. After operator input the configuration parameters such as: Observer name, Data collection site name, Data collection date, Maximum data collection leng th and the degree of significance of the surface slope, the program will make it to the main data sampling form as shown in the Figure 4-6. Data sampling form is the main user interface for data acquisition. When the Sample Module receives the digital data from the parallel port, it wi ll process in real time. When the operator pushes the cart after every 30cm interval, the distance sensor will trigger the communication port and the soft ware to process the data collection and pavement profile presentation. The elevation between the current point and the reference start point is plotted on an oscilloscope si mulation two axis screen. The screen scale changes with the value of elevation a nd refresh after every one hundred meters. Figure 4-6 Data Sampling Form

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52 For direct type I system, when the com puter send a “Get Angle Data” command to the A/D converter, the A/D converter will response with the digital angle data. Each angle data will be filtered depends on conf iguration. On every 30cm’s interval, the processed data will be displayed on the screen and stored into the computer memory. For direct type II system, the software reads the rotary angle all the tim e and records the data when each 12.5cm interval is reached. The ope rator have the option of either interrupting the data sampling procedure for data processing or wait until the maximum distance is reached, then the operator can make the analys is of the sampled data and save the data. Then data file is in our own format and can be transferred to other format according to user’s requirement. 4.3.3 Data Processing Module The data analysis module is the most im portant part of the DAPRES software. It can plot the profile and make an alysis of the profile, then gives the results. The pavement profile can be filtered in order to get the wa velength of interest, revi ewed for all sections or any interested sect ion part, summarized to get the r oughness indices and divided to get the subsection’s analysis results in this module. Figure 4-7 System Configuration Form

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53 Figure 4-7 is the main configuration form Before the data analysis, operator was required to input the configuration parameters that include the longe st wavelength to be kept in the data for further analysis, the valu e of the subsection length and the criteria for the elevation roughness analysis. The operator can review and plot the coll ected data; obtain the summary from the collected data as shown in Figure 4-8 and Figure 4-9. Figure 4-8 Profile Data Review Form Figure 4-9 Analysis Results Summary Form

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54 The important aspect of data analysis is the ability to analyze the subsection’s IRI result as shown in Figure 4-10. The S ubsections’ RN value and Roughness Standard Deviation value can also be calculated and plotted on the screen. The RN and RSD results are similar to IRI results. Th e RN results are shown in Figure 4-11. Figure 4-10 Subsection IRI Value Form Figure 4-11 Subsection RN Value Form

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55 4.3.4 Calibration and Configuration Module 4.3.4.1 System Calibration Due to the errors generated by the system and the original difference between tilt sensor’s surface and absolute surface for direct type I system, field offset calibration is required. The operator can finished the calibra tion procedure according to the instruction in the calibration form shown as Figure 4-12. Figure 4-12 System Calibration Form 4.3.4.2 Draft Calibration The draft calibration is used to solve th e problem that value of the pavement height leaned to one side. Figure 4-13 Draft Calibration Form

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56 Figure 4-13 is the draft calibration fo rm. The operator can finish the draft calibration follow the on-screen instructions. The system will calculate the offset value and save the value for later use. 4.3.4.3 System Configuration The user can change the parameters duri ng data processing to get the expected results. The parameters includes Low pass and high pass bandwidth, IRI/RN, RSD filter bandwidth, sample interval if the wheel circumferences is changed, angle calibration value and etc. Figure 4-14 shows the system parameters setup form. Figure 4-14 System Parameters Setup Form

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58 CHAPTER 5 DATA COLLECTION AND DATA ANALYSIS In order to verify the system functi on of two direct type roughness-measuring systems, we collected several sets of da ta under different conditions using several different profilers (direct type I Profiler, FDOT High-Speed profiler, FACE Dipstick) in Gainesville, FL to evaluate the repeatabilit y, accuracy, and correlation of the systems. We also collected data in Tampa, FL using direct type I and direct type II profilers to evaluate the repeatability and correlation of these tw o profilers. Finally so me conclusions are derived in this chapter. 5.1 Data Collection It was necessary to conduct field experiment to verify whether the direct type I system and direct type II system functi ons well enough to match the design requirements and also to test the correlation results between the direct type I system and direct type II system. In order to verify function of direct type I profiler, field data collection was performed in Gainesville, Florida. Based on the suggestions from FDOT, four FDOT pavement calibration sections were used fo r field roughness data co llection. The site had

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58 roughness ranged from rough to smooth conditi ons. Each section has a length between 500 to 600 feet. All the devices (including di rect type I profiler, FDOT High-speed profiler and FACE Dipstick) were used for da ta collection. Repeated runs of each device were performed on the same calibration sec tion. Except the FACE Dipstick, all other devices had minimum three repeated runs on each FDOT calibration sections to minimize the operational biases. It is a very time-c onsuming process for the FACE Dipstick to collect pavement profile elevation data; only two repeated runs were used for the FACE Dipstick to be operated on each FDOT calibration section. The direct type I profiler was operated at walking speed. The FACE Dipstick was operated even at much lower speed. Since F DOT High-Speed Profiler can be operated at high speed, different operating speeds were used to evaluate the impact of the speed on roughness outputs of the FDOT High-Speed Pr ofiler. The operating Speeds used were 30 mph, 45 mph and 60 mph. The FDOT High-Speed Profiler has four different sampling rates, i.e. sampling interval s of 0.273 feet (Rate 1), 0.545 fe et (Rate 2), 0.818 feet (Rate 3) and 1.091 feet 9 (Rate 4). According the defi nition of RN, it could be anticipated that the sampling rate may have certain impact on RN. Thus, in order to objectively evaluate the measurements of RN by the FDOT HighSpeed Profiler, different sampling rates should be used to assess whether the sampli ng rate has impact on RN, so sampling rates 1 to 4 for the FDOT High-Speed Profiler we re used in field data collection. After verified the repeatability and correla tion of direct type I profiler, 10 test sections were selected and measured using dire ct type I profiler and direct type II profiler in Tampa, Florida. The test sites provide the broad range of roughness.

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59 Each filed section should be straight with no curves or turns, and should have as flat a grade as possible. Lengths of 500 feet (150m) have been commonly used in the United State. Thus, the section length of 500 feet (150 m) was used in the data collection. The test sites are shown in Table 5-1. Table 5-1 Test Sites Locations SITE STREET NAME AVG IRI (inch/mile) 1 MAGNOLIA DR. 147.6 2 LAUREL DR. 216.1 3 N 48th STREET 117.2 4 E 113th STREET 145.1 5 PINE DR 149.5 6 SYCAMORE DR. 168.5 7 L. COLLINS Blvd 135.6 8 ALUMNI DR. 219.9 9 N WOODMERE DR. 205.3 10 SPECTRUM DR. 160.3 When selecting the sites, seve ral concerns were considered: 1. Number of repeat runs. Three repeat r uns were made for each wheel path (left and right wheel path) at each test section. The mean values of the reported roughness statistics were calculated and us ed as the summarized statistic. 2. Raw data reporting interval: The raw data reporting interval of the profiler was fixed at 30 cm for direct t ype I profiler and 11.25cm for di rect type II profiler. The summary statistics were reported for the entire length of a test run, 3. Testing distance. The total testing distan ce for each section was 150m (500 ft).

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60 5.2 Data Analysis 5.2.1 Repeatability Analysis Repeatability refers to the capability of a measuring device to obtain statistically similar results form repeated runs with m easuring conditions unchanged. Repeatability is one of the most important quality measures used to evaluate the performance of a measuring device. For a calibration section in Gainseville, the direct type I profiler was operated for three repeated runs to obtain IRI and RN value on each run. For the FDOT High-Speed Profiler, since different operating speeds and sampling rates were used, thus, for each combination of operating speed and samp ling rate, three repeated runs were used to obtain IRI and RN value on each calibration section. Table 5-2 and Table 5-3 show the RN and IRI values obtained by the direct type I walking pr ofiler from repeated runs on each calibration section. The diffe rence of IRI and RN values between repeated runs was used to quantify the repeatabil ity of direct type I profiler. From Table 5-2 and Table 5-3, it can be seen that the over-all average diffe rence of RN between repeated runs was 0.05 and the overall-all average difference of IRI between repeated runs was 4.5 in/mile. This number means that the direct type I profiler presented good repeatability. Table 5-2 RN Values of Direct Type I Profiler in Each Section Run Section 1Section 4Section 6Section 7 1 3.30 3.00 2.83 3.02 2 3.31 2.95 2.82 2.98 3 3.37 2.97 2.86 3.01 Difference0.07 0.05 0.04 0.04

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61 Table 5-3 IRI Values of Direct Type I Profiler in Each Section Run Section 1 (inch/mile) Section 4 (inch/mile) Section 6 (inch/mile) Section 7 (inch/mile) 1 53 90 175 97 2 55 87 170 93 3 52 91 169 92 Difference 3 4 6 5 Table 5-4 shows the average maximum differen ces of RN values be tween repeated runs of FDOT High-Speed Profiler. The original RN values are presented in Appendix A. From Table 5-4, it can be seen that the over-all average difference between repeated runs was 0.05. Thus, the FDOT High-Speed Prof iler also had good repeatability. Table 5-4 RN Values between Repeated Runs of FDOT High-Speed Profiler 30 mph45 mph60 mphAverage Section 1 Rate 1 0.01 0.06 0.02 Rate 2 0.03 0.02 0.06 Rate 3 0.05 0.01 0.01 Rate 4 0.02 0.02 0.00 Average = 0.03 Section 4 Rate 1 0.12 0.13 0.25 Rate 2 0.02 0.16 0.05 Rate 3 0.06 0.04 0.10 Rate 4 0.02 0.04 0.01 Average = 0.08 Section 6 Rate 1 0.05 0.02 0.05 Rate 2 0.02 0.16 0.05 Rate 3 0.03 0.02 0.04 Rate 4 0.04 0.04 0.07 Average = 0.05 Section 7 Rate 1 0.04 0.02 0.04 Rate 2 0.04 0.02 0.01 Rate 3 0.04 0.04 0.06 Rate 4 0.03 0.03 0.02 Average = 0.03 Over-All Average = 0.05

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62 5.2.2 Correlation Analysis A reliable measuring device should ha ve good correlation with standard reference. If a measuring device has a good co rrelation with standa rd reference and good repeatability, this device is said to be reliable with good measuring performance. 5.2.2.1 RN Correlation Analysis The evaluation on RN mainly focused on the FDOT High-Speed Profiler and direct type I walking profiler because only th ese two devices can get RN values. The RN correlation curves between di rect type I profiler and the FDOT High-Speed Profiler operated at different sampli ng rates and speeds are shown in Figures 5.1 through Figure 5.12. Figure 5-1 Correlation between High-Speed Laser Profiler and Direct Type I Profiler (RN, 30 mph, Rate1) y = 2.6339x 4.6529 R2 = 0.9891 2.50 2.75 3.00 3.25 3.50 3.75 4.00 4.25 4.50 2.752.852.953.053.153.253.353.45 RN from Direct Type I ProfilerRN from High-Speed Laser Profiler

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63 Figure 5-2 Correlation between High-Speed Laser Profiler and Direct Type I Profiler (RN, 45 mph, Rate1) y = 2.6179x 4.6119 R2 = 0.97452.50 2.75 3.00 3.25 3.50 3.75 4.00 4.25 4.502.752.852.953.053.153.253.353.45RN from Direct Type I ProfilerRN from High-Speed Laser Profiler Figure 5-3 Correlation between High-Speed Laser Profiler and Direct Type I Profiler (RN, 60 mph, Rate1) y = 2.7132x 4.9438 R2 = 0.9298 2.50 2.75 3.00 3.25 3.50 3.75 4.00 4.25 4.50 2.752.852.953.053.153.253.353.45 RN from Direct Type I ProfilerRN from High-Speed Laser Profiler

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64 Figure 5-4 Correlation between High-Speed Laser Profiler and Direct Type I Profiler (RN, 30 mph, Rate2) y = 2.5644x 4.2043 R2 = 0.9911 2.50 2.75 3.00 3.25 3.50 3.75 4.00 4.25 4.50 2.752.852.953.053.153.253.353.45 RN from Direct Type I ProfilerRN from High-Speed Laser Profiler Figure 5-5 Correlation between High-Speed Laser Profiler and Direct Type I Profiler (RN, 45 mph, Rate2) y = 2.6358x 4.4338 R2 = 0.9889 2.50 2.75 3.00 3.25 3.50 3.75 4.00 4.25 4.50 2.752.852.953.053.153.253.353.45 RN from Direct Type I ProfilerRN from High-Speed Laser Profiler

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65 Figure 5-6 Correlation Between High-Speed Laser Profiler and Direct Type I Profiler (RN, 60 mph, Rate2) y = 2.6882x 4.5928 R2 = 0.99182.50 2.75 3.00 3.25 3.50 3.75 4.00 4.25 4.50 2.752.852.953.053.153.253.353.45 RN from Direct Type I ProfilerRN from High-Speed Laser Profiler Figure 5-7 Correlation between High-Speed Laser Profiler and Direct Type I Profiler (RN, 30 mph, Rate3) y = 2.4737x 3.8364 R2 = 0.9686 2.50 2.75 3.00 3.25 3.50 3.75 4.00 4.25 4.50 2.752.852.953.053.153.253.353.45 RN from Direct Type I ProfilerRN from High-Speed Laser Profiler

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66 Figure 5-8 Correlation between High-Speed Laser Profiler and Direct Type I Profiler (RN, 45 mph, Rate3) y = 2.5677x 4.1318 R2 = 0.9611 2.50 2.75 3.00 3.25 3.50 3.75 4.00 4.25 4.50 2.752.852.953.053.153.253.353.45 RN from Direct Type I ProfilerRN from High-Speed Laser Profiler Figure 5-9 Correlation between High-Speed Laser Profiler and Direct Type I Profiler (RN, 60 mph, Rate3) y = 2.764x 4.7682 R2 = 0.9955 2.50 2.75 3.00 3.25 3.50 3.75 4.00 4.25 4.50 2.752.852.953.053.153.253.353.45 RN from Direct Type I ProfilerRN from High-Speed Laser Profiler

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67 Figure 5-10 Correlation between High-Speed Laser Profiler and Direct Type I Prof iler (RN, 30 mph, Rate4) y = 2.3646x 3.3224 R2 = 0.9315 2.50 2.75 3.00 3.25 3.50 3.75 4.00 4.25 4.50 4.75 2.752.852.953.053.153.253.353.45 RN from Direct Type I ProfilerRN from High-Speed Laser Profiler Figure 5-11 Correlation between High-Speed Laser Profiler and Direct Type I Profiler (RN, 45 mph, Rate4) y = 2.4674x 3.6696 R2 = 0.92232.50 2.75 3.00 3.25 3.50 3.75 4.00 4.25 4.50 4.75 2.752.852.953.053.153.253.353.45 RN from Direct Type I ProfilerRN from High-Speed Laser Profiler

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68 Figure 5-12 Correlation between High-Speed Laser Profiler and Direct Type I Profiler (RN, 60 mph, Rate4) y = 2.5295x 3.871 R2 = 0.9484 2.50 2.75 3.00 3.25 3.50 3.75 4.00 4.25 4.50 4.75 2.752.852.953.053.153.253.353.45 RN from Direct Type I ProfilerRN from High-Speed Laser Profiler The corresponding correlation coefficients (R2 values) are presented in Table 5-5. From these figures and the table, it is clear ly shown that the correlation between direct type I profiler and the FDOT High-Speed Profil er is good, and it also means that direct type I profiler can calibrate the FDOT High-Speed Profiler. Table 5-5 R2 Values at Different Operating Speeds and Sampling Rates 30 mph 45 mph 60 mph Rate 1 0.9891 0.9745 0.9298 Rate 2 0.9911 0.9889 0.9918 Rate 3 0.9686 0.9611 0.9955 Rate 4 0.9315 0.9223 0.9484 5.2.2.2 IRI Correlation Analysis FACE Dipstick has been considered sta ndard device for field calibration because it has best accuracy performance as compar ed with other automated roughness measuring devices. If a roughness-measuring device has good correlativity with FACE Dipstick, this device is considered having good correl ation with standard reference.

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69 The FACE Dipstick and direct type I walk ing profiler were operated in FDOT test sections 1, 4, 6, and 7. Repeated runs we re performed and the average values from the repeated runs were used for correlation an alysis. Table 5-6 presents the average IRI values from direct type I Pr ofiler and the FACE Dipstick. Table 5-6 IRI Values Collected by FACE Dipstick, Direct Type I Profiler Section FACE Dipstick (inch/mile) Di rect Type I Profiler (inch/mile) 1 48.56 53.27 4 86.76 89.53 6 167.67 170.40 7 104.63 94.43 Figure 5.13 shows the correlation betw een FACE Dipstick and direct type I walking profiler. From this figure, it can be s een that the direct type I walking profiler has good correlativity with FACE Dipstick (R2 = 0.981). Thus, it is reasonable that the correlation between FACE Dipstick and direct type I walking profiler and the correlation between FACE Dipstick is good. Figure 5-13 IRI Correlation betw een FACE Dipstick and Direct Type I Profiler y = x 0.0041 R2 = 0.9809 0 20 40 60 80 100 120 140 160 180 020406080100120140160180 IRI from Direct Type I Profiler (inch./mile)IRI from Dipstick (inch./mile)

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70 Since direct type I profiler showed good correlation with FACE Dipstick as presented previously, direct type I profiler could be used as a standard reference to calibrate FDOT High-Speed Profiler. In this project, one of the main purposes was to analyze the correlativity between direct type I profiler and FDOT High-Speed Profiler. IRI data from FDOT High-Speed Profiler were collected at different sampling rates (rates 1 – 4) and at different speeds (30 mph, 45 mph, and 60 mph). Original data showed that the operating speed had no significant impact on the IRI measurements. However, to analyze the correlativity, correlation results were obtained under different combinations of sampling rate and speed. During field data collection, the FDOT High-Speed Profiler produced roughness data at different wavelengths (bandwidth ), including 300 foot-wavelength and full wavelength (unfiltered bandwidth). Usually, IRI should be processed from pavement surface longitudinal profile with wavelength bandwidth in the range of 200 feet to 500 feet. Thus, the correlation analysis was based on the filtered data with a 300 footwavelength. 1. Correlation for Sampling Rate 1 Figures 5.14 – 5.16 present the correlation anal ysis results for sampling rate 1 at 30 mph, 45 mph, and 60 mph, respectively. From thes e figures, it is found that the correlation between FDOT High-Speed Profiler and direct type I profiler at sampling rate 1 under different operating speeds was good. 2. Correlation for Sampling Rate 2 Figures 5.17 – 5.19 show the co rrelation analysis results for sampling rate 2 at 30 mph, 45 mph, and 60 mph, respectively. Similar to the correlation at sampling rate 2, the

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71 correlation between FDOT High-Speed Profiler and direct type I profil er at sampling rate 2 under different operating speeds was good. 3. Correlation for Sampling Rate 3 Figures 5.20 – 5.22 summarize the correlation an alysis results for sa mpling rate 3 at 30 mph, 45 mph, and 60 mph, respectively. Again, based on the correlation analysis results, it is found that the correla tion between FDOT High-Speed Profiler and direct type I profiler at sampling rate 3 under different operating speeds was good. Figure 5-14 IRI Correlation between Direct Type I Profiler and High-Speed Profiler (30 mph, Sampling Rate 1) y = 0.9605x + 0.335 R2 = 0.9811 0 20 40 60 80 100 120 140 160 180 020406080100120140160180200 IRI from High-Speed Profiler (inch./mile)IRI from Direct Type I Profile r (inch./mile)

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72 Figure 5-15 IRI Correlation between Direct Type I Profiler and High-Speed Profiler (45 mph, Rate 1) y = 0.9685x 0.9976 R2 = 0.9758 0 20 40 60 80 100 120 140 160 180 020406080100120140160180IRI from High-Speed Profiler (inch./mile)IRI from Direct Type I Profiler (inch./mile) Figure 5-16 IRI Correlation between Direct Type I Profiler and High-Speed Profiler (60 mph, Rate 1, 300-ft.) y = 0.9171x + 2.8603 R2 = 0.9795 0 20 40 60 80 100 120 140 160 180 020406080100120140160180200 IRI from High-Speed Profiler (inch./mile)IRI from Direct Type I Profiler (inch./mile)

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73 Figure 5-17 IRI Correlation between Direct Type I Profiler and High-Speed Profiler (30 mph, Rate 2) y = 1.0005x + 0.3538 R2 = 0.9803 0 20 40 60 80 100 120 140 160 180 020406080100120140160180 IRI from High-Speed Profiler (inch./mile)IRI from Direct Type I Profiler (inch./mile) Figure 5-18 IRI Correlation between Direct Type I Profiler and High-Speed Profiler (45 mph, Rate 2) y = 0.9939x 0.212 R2 = 0.977 0 20 40 60 80 100 120 140 160 180 020406080100120140160180 IRI from High-Speed Profiler (inch./mile)IRI from Direct Type I Profile r (inch./mile)

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74 Figure 5-19 IRI Correlation between Direct Type I Profiler and High-Speed Profiler (60 mph, Rate 2) y = 0.9263x + 3.2603 R2 = 0.9731 0 20 40 60 80 100 120 140 160 180 020406080100120140160180200 IRI from High-Speed Laser Profiler (inch./mile)IRI from Direct Type I Profiler (inch./mile) Figure 5-20 IRI Correlation between Direct Type I Profiler and High-Speed Profiler (30 mph, Rate 3) y = 1.0351x 0.0507 R2 = 0.9808 0 20 40 60 80 100 120 140 160 180 020406080100120140160180 IRI from FDOT High-Speed Profiler (inch./mile)IRI from Direct Type I Profiler (inch./mile)

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75 Figure 5-21 IRI Correlation between Direct Type I Profiler and High-Speed Profiler (45 mph, Rate 3) y = 1.0058x + 1.0798 R2 = 0.9766 0 20 40 60 80 100 120 140 160 180 020406080100120140160180 IRI from High-Speed Profiler (inch./mile)IRI from Direct Type I Profiler (inch./mile) Figure 5-22 IRI Correlation between Direct Type I Profiler and High-Speed Profiler (60 mph, Rate 3) y = 0.9146x + 4.5048 R2 = 0.975 0 20 40 60 80 100 120 140 160 180 020406080100120140160180200 IRI from High-Speed Profiler (inch./mile)IRI from Direct Type I Profile r (inch./mile) 5.3 Analysis between Direct Type I and Direct Type II Profilers Based on the conclusions we have got above we can use direct type I profiler to evaluate direct type II profiler. For the data collected during the fi rst period of this study

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76 are not sufficient enough to finish the whol e correlation analysis. Only 10 section sites were selection to conduct data collection and three repeated runs on each section were made at each section. 5.3.1 Repeatability Table 5-7 shows the IRI, RN value collected from the field tests using the direct type I profiler. Table 5-8 show s the IRI, RN value collected from the field tests using the direct type II profiler. The average va lue is derived from the 3 times tests. From the tables, we can see that the direct type I profiler has a very good repeatability, but the direct t ype II profiler does not has as good repeatability as direct type I profiler. This problem is due to the resolution of A/D converter of the system. So using a high resolution (like 16 bit or higher) A/D converter will be better. Table 5-7 RN and IRI Values of Direct Type I Profiler IRI Results (inch/mile) RN Results 1 2 3 Difference1 2 3 Difference Section 1 145.1 147.0150.25.1 2.72 2.73 2.79 0.07 Section 2 218.0 214.8216.13.2 2.02 2.10 1.99 0.11 Section 3 114.0 121.0116.67.0 3.06 3.09 3.15 0.09 Section 4 143.2 144.5148.35.1 2.95 2.95 2.94 0.01 Section 5 145.1 150.2153.38.2 2.74 2.72 2.79 0.07 Section 6 166.6 169.2170.43.8 2.58 2.59 2.64 0.06 Section 7 135.0 135.0136.91.9 3.13 3.15 3.17 0.04 Section 8 219.9 218.6221.12.5 2.09 2.11 2.03 0.08 Section 9 203.4 205.9207.23.8 2.64 2.60 2.57 0.07 Section 10 159.0 159.7162.83.8 2.00 1.98 1.93 0.07

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77 Table 5-8 RN and IRI Values of Direct Type II Profiler IRI Results (inch/mile) RN Results 1 2 3 Difference1 2 3 Difference Section 1 160.3 177.4 164.1 17.1 2.47 2.33 2.72 0.39 Section 2 302.9 297.2 309.2 12.0 1.45 1.95 1.67 0.17 Section 3 133.7 119.8 135.6 15.8 2.82 3.02 2.81 0.21 Section 4 120.4 122.3 115.3 8.2 2.84 2.92 2.92 0.08 Section 5 230.0 247.1 214.8 32.3 1.74 1.74 1.86 0.12 Section 6 271.2 273.1 293.4 22.2 2.74 2.66 2.54 0.20 Section 7 88.1 84.9 87.4 3.2 3.43 3.37 3.32 0.11 Section 8 314.9 307.9 335.2 27.2 1.79 1.86 1.75 0.11 Section 9 349.1 355.4 331.4 24.1 1.55 1.46 1.74 0.28 Section 10 281.3 283.2 263.6 19.6 2.47 2.33 2.72 0.30 5.3.2 Correlation Analysis Table 5-9 is the average IRI and RN values from direct type I profiler and direct type II profiler. Figure 5.23 and Figure 5.24 show the correla tion analysis between direct type I and direct type II profilers. Table 5-9 RN and IRI Values Collected by Direct Type I and Type II Profiler Section Direct Type II Prof iler Direct Type I Profiler Ave. IRI (inch/mile) Ave. RN Ave. IRI (inch/mile) Ave. RN 1 167.3 2.51 147.6 2.75 2 302.9 1.69 216.1 2.04 3 129.9 2.88 117.2 3.10 4 119.1 2.89 145.1 2.95 5 230.6 1.78 149.5 2.75 6 279.4 2.65 168.5 2.60 7 86.8 3.37 135.6 3.15 8 319.3 1.8 219.9 2.08 9 345.3 1.58 205.3 2.60 10 276.2 1.59 160.3 1.97

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78 Figure 5-23 IRI Correlation between Direct Type I and Direct Type II Profiler y = 0.3289x + 92.273 R2 = 0.74190 50 100 150 200 250050100150200250300350400 IRI From Direct Type II Profiler (inch./mile)IRI From Direct Type I Profiler (inch./mile) Figure 5-24 RN Correlation between Direct Type I and Direct Type II Profiler y = 0.5346x + 1.3834 R2 = 0.656 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 11.251.51.7522.252.52.7533.253.5 RN From Direct Type II ProfilerRN From Direct Type I Profil e From the figure, it can be seen that di rect type II profiler has good correlativity with direct type I profiler (R2 = 0.7419 for IRI, R2 = 0.656 for RN). More research needs to be done to improve the correlativity.

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80 CHAPTER 6 SUMMARY, CONCLUSION AND RECOMMENDATION 6.1 Summary Two Direct Type pavement roughness evalua tion systems were developed in this research and study. Both the Direct Type pr ofilers are inexpensiv e, light weighed and easily transportable devices that can be used by a surveyor. It is designed to facilitate the efficient data collection and presentation of pavement surface information, including pavement profile, grade and roughne ss index such as IRI and RN. The purpose of this research was to deve lop the two Direct Type road roughness evaluation system and evaluate whether the two Direct Type profilers has good correlation to Dipstick and can be used to calibrate High-Speed Profiler. To reach the purpose, standard roughness measuring systems were used as refere nces in fields to evaluate whether the High-Speed Profiler had good correlation w ith these standard references. The reference measurements included IRI valuates collected by FACE Dipstick, Direct Type I Profiler from 4 test sections in Gainesville, Florida. However, since FACE Dipstick do not have the func tion to produce RN values, only the Direct Type I Walking Profiler was used to evalua te the High-Speed Profiler’s performance in measuring RN values.

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80 Field tests were performed in Gaines ville, Florida. Four FDOT calibration sections were measured by the FDOT High-Sp eed Profiler, Direct Type-I Profiler, FACE Dipstick. The corresponding IRI values and corresponding RN values were obtained. The FDOT High-Speed Profiler was operated at different sampling rate s (rates 1–4) and at different speeds (30 mph, 45 mph, and 60 mph). All devices were operated for at least three repeated runs. After field data were obtai ned, data analysis was pe rformed to evaluate the measuring performance of the FDOT High-Sp eed Profiler in obtaining RN values. The performance was evaluated based on: the im pact of sampling rate, repeatability, and correlativity with Direct Type I Walking Prof iler, etc. The repeatability of the Direct Type I Walking Profiler was also evaluated. Linear regression analysis (correlation analysis) was performed to evaluate the IRI correlativity between Direct Type-I pr ofiler and FACE Dipstick. The correlation analysis was also performed to evaluate th e RN correlativity between Direct Type-I profiler and FDOT High-Speed Profiler. The FDOT High-Speed Profiler was also operated at different sampling rates and different speeds. To evaluate IRI and RN correlativity betw een Direct Type I and Direct Type II profilers, a field-testing and data collectio n work was conducted around USF area. The Direct Type profilers were run along the same marked wheel path of all the 10 section. Finally, linear regression analysis was perf ormed to evaluate IRI and RN correlativity between Direct Type I profiler and Direct Type II profiler, with the results showing correlation between the outputs of the two Direct Type systems.

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81 6.2 Conclusions From data analysis, it was found that Di rect Type I Walki ng Profiler showed satisfactory repeatability performances. Thus, for real data collection by Direct Type I system, if the data collection procedure is we ll controlled, there is no need to run these devices more than three repeated runs b ecause the difference between different runs could be ignored. FDOT High-Speed Profiler could be opera ted at different operating speeds (30 mph – 60 mph) with very little difference in RN values for a gi ven test section and sampling rate. However, any speeds beyond the speed range may not produce the same conclusion because the analysis was based on the speed range and no conclusion is supported if the speed is beyond the speed range. The FDOT High-Speed Profiler and Dir ect Type I Walking Profiler had good correlations at different sampling rates a nd operating speeds of the FDOT High-Speed Profiler. Since the Direct Type Walking Pr ofiler is considered the type I roughness measuring device, it can be used to calibrate the FDOT High-Speed Profiler’s RN outputs. This conclusion could make the pr ocedure to measure pavement surface RN values more efficiently and effectively. Correlation analysis showed that all the roughness measuring de vices used in the project had good correlations be tween them in terms of IRI. Thus, the FDOT high speed Profiler could be calibrated by Di rect Type I profiler. The di rect type II profiler is not good enough to calibrate High-Speed pr ofiler and needs to be improved.

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82 6.3 Recommendations Some recommendations are presented here based on the analysis and system development experience. They could improve the performance of these systems and make it more precise. The major errors of Direct Type I system come from the limitations of the sensor inertial response. The new sensor technol ogy may be employed to minimize or even diminish the current sensor limitations. It is recommended that the high-resolution analog to digital data acquisition system or a new rotary angle sensor be used in Direct Type II prof iler. Then it could get the satisfactory correlation result between Dir ect Type I and Direct Type II profilers and it can be used to calibrate the High-Speed profiler. Since the impact of sampling rate of the High-Speed Profiler had significant impact on its RN output, to measure RN va lues, the High-Speed Profiler should be operated at a specified sampling rate. To best use the High-Speed Profiler to measure RN values, further research is needed to verify the best sampling rate. Based on the discussions presented above, it is recommended that sampling rate 1 or sampling rate 2 be used when measuring RN values. It is recommended that more research be conducted in attempt to get more useful pavement data for correlation verification betwee n Direct Type I and II profilers and Face Dipstick, High Speed Profiler.

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REFERENCES 1. Yoder, E. J., Hampton D., “Pavement Profile and Roughness Measurements; A Review of Method”, Purdue University, January, 1958 2. W. Sayers, T.D.Gillespie, and W.D.O.Paterson., “Guidelines for the Conduct and Calibration of Road Roughness Measuremen ts. World Bank Technical Paper 46”, The World Bank, Washington, DC.,1986 3. Lu. J., Carl B. Bertrand, and W. Ronald Hudson, “Evaluation and implementation of the roughness Measuring Subsystem of the ARAN Unit.”, University of Texas at Austin, February, 1991 4. Lu, J., “Spectral Analysis of Vehicle Sp eed Characteristics”. In Transportation Research Record 1375, National Resear ch Council, Washington D.C. 1992 5. Michael W. Sayers Steven M. Karamihas ., “The Little Book of Profiling”, September 1998 6. Lu. J., et al.,“Speed Effect Analysis a nd Canceling Model of a Response-Type Road Roughness Measuring System”, Trans portation Research Record 1260, 1990 7. S.M Karamihas etc., “Report 434 Guidelin es for Longitudinal Pavement Profile Measurement”, Transportation Resear ch Board, National Academy Press, Washington, D.C., 1999 8. Min Zhou, “Pavement Condition Analysis Based on Response-Type Road Roughness Measurement System and Manual System”, Ma ster Thesis, Univ. of South Florida, 1999 9. Sayers,M.W., “On the Calculation of International Roughness Index from Longitudinal Road Profile.” Transportati on Research Record 1501, National Research Council, Washington, D.C., 1995 10. Bennett C.R., “Calibrating Road Roughness Meters in Developing Countries”, Transportation Research Record 1536

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11. Gillespie, T.D., Sayers, M.W., and Segel, L ., “Calibration of Response Profile Data”, Federal Highway Administration, 1996 12. Janoff, M.S., “Pavement Roughness and Ri deability Field Evaluation”, NSHRP Report 308, National Research Council, Washington, D.C., 1998 13. Lu, J., Hudson, W. R., and Bertrand, C., "E valuation of the Roughness System of the Automatic Road Analyzer", Tran sportation Research Record 1435, 1994 14. M.Y.Shahin., “Pavement Management for Airport, Roads, and Parking Lots”,1994