This study aimed at investigating the current practices and methods adopted by roads agencies around the world with regard to collection, analysis and utilization of the data elements pertaining to the main pavement condition indicators in pavement management systems (PMS). It also aimed at identifying the main predictors associated with each condition indicator and the factors that govern pavement structural and functional performance. Development of a new performance index that incorporates parameters or measures related to the main condition indicators (surface defects, roughness, deflection and skid resistance) and establishing the weight to be assigned to each indicator based on the relative impact on pavement condition was also one of the main objectives of this study. Thousands of pavement sections were subjected to thorough testing and inspection over the last few years to collect data pertaining to the main condition indicators. The collected data encompass visual distress survey, deflection measurements, roughness and skid resistance measurements. Collection of various condition indicators was accomplished according to well known international standards. The collected data were processed, tabulated and analyzed for the purpose of development of performance models and to prove certain theories or good practices. / Advanced tools and machines were utilized to collect these data with a high degree of accuracy. The Falling Weight Deflectometer (FWD) was used to collect deflection data for structural analysis. Two Non-contact laser roughness measuring devices mounted on vehicles were heavily used for collecting roughness, texture, and rutting data. Distress data were collected using a manual procedure adopted and standardized at the Pavement Management System Unit of Dubai Emirate. Powerful engineering and statistical softwares were used in the analysis for the purpose of processing the data, back calculating the main parameters pertaining to pavement response, establishing the correlation matrices between various dependent variables and their predictors, and finally, applying linear and non linear regression analysis to develop reliable and predictable deterioration models for the uses of pavement management system. The analysis procedure was supplemented by a vast literature review for the up to date information along within deep investigations and verifications for some of the current practices, theories and models used in pavement design and pavement evaluation with more emphasis on the inherent drawbacks associated to these models and procedures. The study confirmed that pavement condition deterioration and performance can be best predicted and evaluated based on four main condition indicators; First, surface distress to assess the physical condition of the pavements and detect the inherent problems and defects caused by various factors affecting pavement performance. Second; roughness measurements to evaluate the riding quality of the pavement. / Third; deflection to calculate pavement response (stress and strains) and to assess pavement structural capacity and calculating the remaining life, and finally, skid resistance measurement to assess the level of safety and surface texture properties. Thorough study and investigation of the physical condition indicators and the associated parameters, confirmed that pavement distress data are vital elements in each pavement management system. Distress data can be used effectively to identify the main problems associated with pavement performance, causes of deterioration, maintenance measures needed to prevent the acceleration of the distress, the rehabilitation schemes needed to improve the pavement condition and finally to prepare maintenance work programs and to estimate the annual maintenance needs under an open or limited budget. Alligator cracking was found to have the heaviest impact on pavement condition. Distress density, probable causes of deterioration and distress propagation rate are the required parameters in PMS. Roughness was found to have a basic influence on pavement condition and the type of selected treatment. The use of Roughness data in terms of International Roughness Index (IRI) can be optimized in PMS by using this indicator in the following forms: / Roughness, as an objective measure, can be used as a good performance predictor of the current riding quality of pavements in service and reflects the inherent imperfections and built-in irregularities embodied in the road pavement surface. Roughness measurement can be used as a reference to establish construction specifications and provides through the PMS system an organized feedback approach to correct the persistent design deficiencies detected after road construction. Roughness can be used effectively in the planning process for maintenance works and to select the candidate sections through calculating the functional remaining life based on the estimated terminal value using Roughness-Age, Roughness-ESAL, and Roughness-PSI models. Lane–IRI along with the Difference between the left and right wheel IRI values, termed as “ Yaw” are the most suitable forms to be used in PMS to report about roughness characteristics. Yaw term can be used effectively to report or feed back about geometric imperfections that exist on the road surface such as improper cross slope, shoving and the probable drainage problems. The roughness cumulative distribution curves can be used as a planning tool in PMS to report at the network level. These curves indicate the network health and the required funding at different level of risks, so proactive measures can be taken and the required budgets can be made available. / Deflection data were found to form a basic component of the PMS. It was found that these data can be used at both network and project levels. Direct deflection measurements were found Not to be the ideal form to report about structural capacity at the network level. It is rather can be used at project level to detect weak spots and critical pavements layers. At the network level, the back calculated parameters from deflection basin such as Pavement Modulus (Ep), Asphalt and Pavement Curvature (CUR), Cross Sectional Area and the other deflection basin characteristics are much more appropriate for reporting about pavement structural conditions and calculating the structural remaining life in PMS. The design deflection and curvature that characterize the pavement have been found to be calculated based on the mean along with the two times the standard deviation of the measured data. The Effective Structural Number (SNeff) was found to have good correlations with the Total Pavement Thickness (Ht), the value of the deflection measured at the center of the loading plate ( D0 ) and the difference between D0 and the deflection measured at 450mm from the center of the loading plate ( D0 - D450 ). The first two variables were found to account for more than 92% of the structural capacity prediction model. / Traffic variable in terms of the accumulated standard repetitions (ESAL) was found to account for more than 60% of the deflection model predictability. Other variables such as E value, asphalt and base layer thicknesses can improve the predictability of the model if included. The concept of the relative value of effective pavement modulus to the original pavement modulus (Eeff/E0) was found to gives a reliable representation about the exhausted and the remaining life of the in-service pavement structure. The study showed that the pavement is reported to be structurally failed, when the effective asphalt or pavement modulus is about 20 - 35 % of its original design value which is equal to the modulus of the unbound material. It was also found that when the area of the fatigue cracking and the patching distresses exceeds 17% of the total pavement section area, or the depth of rutting is more than 15mm, the pavement is reported to be structurally failed and major rehabilitation or reconstruction should be applied. Skid resistance can be reported in the form of International Friction Index (IFI), as a well defined universal index, along with other two numbers; F60 Friction (Microtexture) related number measured at 60 km/h velocity and Macrotexture related number and Vp, which constitute the IFI index can be used in Pavement management system applications to report about skid resistance characteristics and the network level of safety. These three figures can be used to report about pavement condition, accidents, airports operations, and maintenance management surveys. / In this study, new methods and models were developed and suggested to be used in PMS as an alternative to the current available methods which were found to be impractical in certain cases. Finally, further research efforts are recommended to explore the uses of other parameters in particular those related to deflection basin analysis, cross sectional area, curvature, and pavement moduli. Skid resistance testing and reporting method should be subjected to further research works for the purpose of standardizing reporting methods, identifying the relative impact of main predictors i.e. megatexture, macrotexture and microtexture components and to develop performance models.

The use of high technology in common daily tasks is boarding all areas of civil engineering; pavement evaluation is not the exception. Accordingly, current pavement imaging systems have been able to collect images at highway speeds and with the use of proper software, this digital information can be translated into pavement distress reports in which all distresses are classified and presented by their type, extent, severity, and location. However, a number of issues regarding the quality of pavement images and the appropriate conditions to acquire them, remain to be addressed. These issues surfaced during the development of a pavement evaluation vehicle for the Florida Department of Transportation (FDOT). The work involved in this thesis proposes basic criteria to evaluate the performance of pavement imaging systems. Mainly four parameters (1) spatial resolution, (2) brightness resolution, (3) optical distortion, and (4) signal to noise ratio, have been identified to assess the quality of a pavement imaging system. First, each of the four parameters is studied in detail in USF's Visual Imaging Laboratory to formulate relevant criteria that can be used to evaluate imaging systems. Then, the developed criteria are used to evaluate the FDOT Survey Vehicle's pavement imaging system. The evaluation speed does not seem to have any significant influence on the spatial resolution, brightness resolution and signal to noise ratio. Little or no optical distortion was observed on the images on wheel paths. Limitations of the imaging system were also determined in terms of the brightness resolution and noise. The conclusions drawn from this study can be used to (1) enhance pavement imaging systems and (2) setup appropriate guidelines to perform automated distress surveys, under varying lighting conditions and speeds to obtain good quality images.

Master of Science / Department of Electrical and Computer Engineering / Dwight D. Day / Balasubramaniam Natarajan / This thesis presents an automated pavement crack detection and classification system via image processing and pattern recognition algorithms. Pavement crack detection is important to the Departments of Transportation around the country as it is directly related to maintenance of pavement quality. Manual inspection and analysis of pavement distress is the prevalent method for monitoring pavement quality. However, inspecting miles of highway sections and analyzing each is a cumbersome and time consuming process. Hence, there has been research into automating the system of crack detection. In this thesis, an automated crack detection and classification algorithm is presented. The algorithm is built around the statistical tool of Principal Component Analysis (PCA). The application of PCA on images yields the primary features of cracks based on which, cracked images are distinguished from non-cracked ones. The algorithm consists of three levels of classification: a) pixel-level b) subimage (32 X 32 pixels) level and c) image level. Initially, at the lowermost level, pixels are classified as cracked/non-cracked using adaptive thresholding. Then the classified pixels are grouped into subimages, for reducing processing complexity. Following the grouping process, the classification of subimages is validated based on the decision of a Bayes classifier. Finally, image level classification is performed based on a subimage profile generated for the image. Following this stage, the cracks are further classified as sealed/unsealed depending on the number of sealed and unsealed subimages. This classification is based on the Fourier transform of each subimage. The proposed algorithm detects cracks aligned in longitudinal as well as transverse directions with respect to the wheel path with high accuracy. The algorithm can also be extended to detect block cracks, which comprise of a pattern of cracks in both alignments.

Pavement Management Systems (PMS) are widely used by transportation agencies to maintain safe, durable and economic road networks. PMS prioritize the maintenance and rehabilitation of pavement sections by evaluating pavement performance at the network level. There are many PMS software packages that have been developed over the past decades for provincial/state road agencies. However, sometimes due to lack of budget and experience, adopting the existing PMS for a road agency is not cost effective. Thus, it is important to introduce a simple, effective, and affordable PMS for a local agency and municipality. This research is carried out in partnership between the City of Markham and the Centre for Pavement and Transportation Technology (CPATT) located at the University of Waterloo. For the purpose of developing a PMS for local agencies, an extensive literature review on PMS components was carried out, with emphasizing data inventory, data collection, and performance evaluation. In addition, the literature review also concentrated on the overall pavement condition assessment. In July 2011, a study on “Evaluation of Pavement Distress Measurement Survey” was conducted as a part of this research and was distributed to cities and municipalities across Canada. The study focused on the current state-of-the-practice in pavement distress and condition evaluation methods used by local agencies to compare the results from the literature review. The components of the proposed PMS framework are also developed based on the literature review with some modifications and technical requirements. The City of Markham is selected as a case study, since it represents a local agency and provides all the data, to illustrate the validation of the proposed PMS framework.

Pavement Management Systems (PMS) are widely used by transportation agencies to maintain safe, durable and economic road networks. PMS prioritize the maintenance and rehabilitation of pavement sections by evaluating pavement performance at the network level. There are many PMS software packages that have been developed over the past decades for provincial/state road agencies. However, sometimes due to lack of budget and experience, adopting the existing PMS for a road agency is not cost effective. Thus, it is important to introduce a simple, effective, and affordable PMS for a local agency and municipality. This research is carried out in partnership between the City of Markham and the Centre for Pavement and Transportation Technology (CPATT) located at the University of Waterloo. For the purpose of developing a PMS for local agencies, an extensive literature review on PMS components was carried out, with emphasizing data inventory, data collection, and performance evaluation. In addition, the literature review also concentrated on the overall pavement condition assessment. In July 2011, a study on “Evaluation of Pavement Distress Measurement Survey” was conducted as a part of this research and was distributed to cities and municipalities across Canada. The study focused on the current state-of-the-practice in pavement distress and condition evaluation methods used by local agencies to compare the results from the literature review. The components of the proposed PMS framework are also developed based on the literature review with some modifications and technical requirements. The City of Markham is selected as a case study, since it represents a local agency and provides all the data, to illustrate the validation of the proposed PMS framework.

Current pavement performance prediction models are based on the parameters such as climate, traffic, environment, material properties, etc. while all these factors are playing important roles in the performance of pavements, the quality of construction and production are also as important as the other factors. The designed properties of Hot Mix Asphalt (HMA) pavements, known as flexible pavements, are subjected to change during production and construction stages. Therefore, most of the times the final product is not the exact reflection of the design. In almost any highway project, these changes are common and likely to occur from different sources, by various causes, and at any stage. These changes often have considerable impacts on the long-term performance of a project. The uncertainty of the traffic and environmental factors, as well as the variability of material properties and pavement structural systems, are obstacles for precise prediction of pavement performance. Therefore, it is essential to adopt a hybrid approach in pavement performance prediction and design; in which deterministic values work along with stochastic ones. Despite the advancement of technology, it is natural to observe variability during the production and construction stages of flexible pavements. Quality control programs are trying to minimize and control these variations and keep them at the desired levels. Utilizing the information gathered at the production and construction stages is beneficial for managers and researchers. This information enables performing analysis and investigations of pavements based on the as-produced and as-constructed values, rather than focusing on design values. This study describes a geo-relational framework to connect the pavement life-cycle information. This framework allows more intelligent and data-driven decisions for the pavements. The constructed geo-relational database can pave the way for artificial intelligence tools to help both researchers and practitioners having more accurate pavement design, quality control programs, and maintenance activities. This study utilizes data collected as part of quality control programs to develop more accurate deterioration and performance models. This data is not only providing the true perspective of actual measurements from different pavement properties but also answers how they are distributed over the length of the pavement. This study develops and utilizes different distribution functions of pavement properties and incorporate them into the general performance prediction models. These prediction models consist of different elements that are working together to produce an accurate and detailed prediction of performance. The model predicts occurrence and intensity of four common flexible pavement distresses; such as rutting, alligator, longitudinal and transverse cracking along with the total deterioration rate at different ages and locations of pavement based on material properties, traffic, and climate of a given highway. The uniqueness of the suggested models compared to the conventional pavement models in the literature is that; it carries out a multiscale and multiphysics approach which is believed to be essential for analyzing a complex system such as flexible pavements. This approach encompasses the discretization of the system into subsystems to employ the proper computational tools required to treat them. This approach is suitable for problems with a wide range of spatial and temporal scales as well as a wide variety of different coupled physical phenomena such as pavements. Moreover, the suggested framework in this study relies on using stochastic and machine learning techniques in the analysis along with the conventional deterministic methods. In addition, this study utilizes mechanical testing to provide better insights into the behavior of the pavement. A series of performance tests are conducted on field core samples with a variety of different material properties at different ages. These tests allow connecting the lab test results with the field performance survey and the material, environmental and loading properties. Moreover, the mix volumetrics extracted from the cores assisted verifying the distribution function models. Finally, the deterioration of flexible pavements as a result of four different distresses is individually investigated and based on the findings; different models are suggested. Dividing the roadway into small sections allowed predicting finer resolution of performance. These models are proposed to assist the highway agencies s in their pavement management process and quality control programs. The resulting models showed a strong ability to predict field performance at any age during the pavements service life. The results of this study highlighted the benefits of highway agencies in adopting a geo-relational framework for their pavement network. This study provides information and guidance to evolve towards data-driven pavement life cycle management consisted of quality pre-construction, quality during construction, and deterioration post-construction. / Civil Engineering

Traditional asphalt mix design procedures are empirical and need random and lengthy trials in a laboratory, which can cost much labor, material resources, and finance. The initiative (Material Genome initiative) was launched by President Obama to revitalize American manufacturing. To achieve the objective of the MGI, three major tools which are computational techniques, laboratory experiments, and data analytics methods are supposed to have interacted. Designing asphalt mixture with laboratory and computation simulation methods has developed in recent decades. With the development of data science, establishing a new design platform for asphalt mixture based on data-driven methods is urgent. A balanced mix design, defined as an asphalt mix design simultaneously considering the ability of asphalt mixture to resist pavement distress, such as rutting, cracking, IRI (international roughness index), etc., is still the trend of future asphalt mix design. The service life of asphalt pavement mainly depends on the properties of the asphalt mixture. Whether asphalt mixture has good properties also depends on advanced asphalt mix design methods. Scientific mix design methods can improve engineering properties of asphalt mixture, further extending pavement life and preventing early distress of flexible pavement. Additionally, in traditional asphalt mix design procedures, the capability to resist pavement distress (rutting, IRI, and fatigue cracking) of a mixture is always evaluated based on laboratory performance tests (Hamburg wheel tracking device, Asphalt Pavement Analyzer, repeated flexural bending, etc.). However, there is an inevitable difference between laboratory tests and the real circumstance where asphalt mixture experiences because the pavement condition (traffic, climate, pavement structure) is varying and complex. The successful application examples of machine learning (ML) in all kinds of fields make it possible to establish the predictive models of pavement distress, with the inputs which contain asphalt concrete materials properties involved in the mix design process. Therefore, this study utilized historical data acquired from laboratory records, the LTPP dataset, and the NCHRP 1-37A report, data analytics and processing methods, as well as ML models to establish pavement distress predictive models, and then developed an automated and balanced mix design procedure, further lying a foundation to achieve an MGI mix design in the future. Specifically, the main research content can be divided into three parts:1. Established ML models to capture the relationship between properties of the binder, aggregates properties, gradation, asphalt content (effective and absorbed asphalt content), gyration numbers, and mixture volumetric properties for developing cost-saving Superpave and Marshall mix design methods; 2. Developed pavement distress (rutting, IRI, and fatigue cracking) predictive models, based on the inputs of asphalt concrete properties, other pavement materials information, pavement structure, climate, and traffic; 3. Proposed and verified an intelligent and balanced asphalt mix design procedure by combining the mixture properties prediction module, pavement distress predictive models and criteria, and non-dominated Sorting genetic algorithm-Ⅱ (NSGA-Ⅱ). It was discovered determining total asphalt content through predicting effective and absorbed asphalt content indirectly with ML models was more accurate than predicting total asphalt content directly with ML models; Pavement distress predictive models can achieve better predictive results than the calibrated prediction models of Mechanistic-Empirical Pavement Design Guide (MEPDG); The design results for an actual project of surface asphalt course suggested that compared to the traditional ones, the asphalt contents of the 12.5 mm and 19 mm Nominal Maximum Aggregate Size (NMAS) mixtures designed by the automated mix design procedure drop by 7.6% and 13.2%, respectively; the percent passing 2.36 mm sieve of the two types of mixtures designed by the proposed mix design procedure fall by 17.8% and 10.3%, respectively. / Doctor of Philosophy / About 96% of roads are paved with asphalt mixture. Asphalt mixture consists of asphalt, aggregates, and additives. Asphalt mix design refers to the process to determine the proper proportion of aggregates, asphalt, and additives. Traditional asphalt mix design procedures in laboratories are empirical and cost much labor, material resources, and finance. Pavement distresses, for example, cracks are important indicators to assess pavement condition. With the development of data science, machine learning (ML) has been applied to various fields by predicting desired targets. The multi-objective optimization refers to determining the optimal solution of a multiple objectives problem. The study applied ML methods to predict asphalt mixture components' proportions and pavement distress with historical experimental data and pavement condition records from literature and an open-source database. Specifically, the main research content can be divided into three parts:1. Established ML models to predict the proportion of asphalt when aggregates are given; 2. Built ML models to predict pavement distress from pavement materials information, pavement structure, climate, and traffic; 3. Develop a digital asphalt mix design procedure by combining the pavement distress prediction models and a multi-objective optimization algorithm.

As the cost of designing and building new highway pavements increases and the number of new construction and major rehabilitation projects decreases, the importance of ensuring that a given pavement design performs as expected in the field becomes vital. To address this issue in other fields of civil engineering, reliability analysis has been used extensively. However, in the case of pavement structural design, the reliability component is usually neglected or overly simplified. To address this need, the current dissertation proposes a framework for estimating the reliability of a given pavement structure regardless of the pavement design or analysis procedure that is being used. As part of the dissertation, the framework is applied with the Mechanistic-Empirical Pavement Design Guide (MEPDG) and failure is considered as a function of rutting of the hot-mix asphalt (HMA) layer. The proposed methodology consists of fitting a response surface, in place of the time-demanding implicit limit state functions used within the MEPDG, in combination with an analytical approach to estimating reliability using second moment techniques: First-Order and Second-Order Reliability Methods (FORM and SORM) and simulation techniques: Monte Carlo and Latin Hypercube Simulation. In order to demonstrate the methodology, a three-layered pavement structure is selected consisting of a hot-mix asphalt (HMA) surface, a base layer, and subgrade. Several pavement design variables are treated as random; these include HMA and base layer thicknesses, base and subgrade modulus, and HMA layer binder and air void content. Information on the variability and correlation between these variables are obtained from the Long-Term Pavement Performance (LTPP) program, and likely distributions, coefficients of variation, and correlation between the variables are estimated. Additionally, several scenarios are defined to account for climatic differences (cool, warm, and hot climatic regions), truck traffic distributions (mostly consisting of single unit trucks versus mostly consisting of single trailer trucks), and the thickness of the HMA layer (thick versus thin). First and second order polynomial HMA rutting failure response surfaces with interaction terms are fit by running the MEPDG under a full factorial experimental design consisting of 3 levels of the aforementioned design variables. These response surfaces are then used to analyze the reliability of the given pavement structures under the different scenarios. Additionally, in order to check for the accuracy of the proposed framework, direct simulation using the MEPDG was performed for the different scenarios. Very small differences were found between the estimates based on response surfaces and direct simulation using the MEPDG, confirming the accurateness of the proposed procedure. Finally, sensitivity analysis on the number of MEPDG runs required to fit the response surfaces was performed and it was identified that reducing the experimental design by one level still results in response surfaces that properly fit the MEPDG, ensuring the applicability of the method for practical applications. / text

abstract: The major challenge for any pavement is the freight transport carried by the structure. This challenge is expected to increase in the coming years as freight movements are projected to grow and because these movements account for most of the load related distresses for the pavement. Substantial effort has been devoted to identifying the impacts of these future national freight trends with respect to the environment, economic growth, congestion, and reliability. These are all important aspects relating to the freight question, but an equally important and often overlooked aspect of this issue involves the impact of freight trends on the physical infrastructure. This study analyzes the impact of future freight traffic trends on 26 major interstates representing 68% of the total system mileage and carrying 80% of the total national roadway freight. The pavement segments were analyzed using the Mechanistic Empirical Pavement Design Guide software after collecting the relevant traffic, climate, structural, and material properties. Comparisons were drawn between the expected pavement performance using current design standards for traffic growth and performance predictions that incorporated more detailed freight projections which themselves considered job growth and six key drivers of freight movement. The differences in the resultant performance were used to generate maps that provide a bird’s eye view of locations that are especially vulnerable to future trends in freight movement. The analysis shows that the areas of greatest vulnerability include segments that are directly linked to the busiest ports, and surprisingly those from Atlantic and Central states that provide long distance connectivity, but do not currently carry the highest traffic volumes. / Dissertation/Thesis / Masters Thesis Civil and Environmental Engineering 2016

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