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Os modelos de previsão de desempenho de pavimentos são importantes ferramentas utilizadas pelos sistemas de gerência, essenciais para o planejamento das atividades de manutenção e reabilitação, assim como para a estimativa dos recursos necessários para a preservação das rodovias. Este trabalho tem por objetivo comparar modelos de desempenho de pavimentos, desenvolvidos por análises empíricas e empírico-mecanísticas, que predizem a evolução da condição de pavimentos flexíveis, ao longo do tempo e/ou tráfego acumulado. Os modelos de desempenho analisados foram desenvolvidos por pesquisadores e órgãos rodoviários brasileiros e internacionais, inclusive os modelos de deterioração utilizados pelo programa computacional de gerência de pavimentos desenvolvido pelo Banco Mundial, o HDM–4 (Highway Development and Management). A pesquisa está baseada na comparação do desempenho real de seções de pavimentos rodoviários, obtido a partir da base de dados dos experimentos LTPP (Long-Term Pavement Performance) do FHWA (Federal Highway Administration), com o comportamento previsto pelos modelos de desempenho desenvolvidos por Queiroz (1981), Paterson (1987), Marcon (1996) e Yshiba (2003). Neste trabalho, a análise do comportamento das seções de teste LTPP-FHWA é feita utilizando-se uma programação fatorial que, através da análise de variância (ANOVA), permite a determinação do nível de significância de fatores pré- selecionados (variáveis independentes: tráfego, idade e número estrutural corrigido) bem como a modelagem do desempenho dos pavimentos dessas seções (variáveis dependentes: irregularidade longitudinal e deformação permanente). / The pavement performance prediction models are important tools used for pavement management, essential for the planning of maintenance and rehabilitation activities, as well as for budgeting. The aim of this work is to compare performance prediction models developed through empirical and empirical-mechanistic analyses, which predict the evolution of the condition of flexible pavements, throughout the time and/or accumulated traffic. The performance prediction models analyzed were developed by researchers and Brazilian and international road agencies, including the deterioration models used by the pavement management comuputer program HDM-4 (Highway Development and Management), developed by the World Bank. The research is based on the comparison of the real performance of pavement of sections, obtained from the data base of the LTPP Program (Long-Term Pavement Performance) of FHWA (Federal Highway Administration), with the behavior predicted by deterioration models developed by Queiroz (1981), Paterson (1987), Marcon (1996) and Yshiba (2003). In this work, the analysis of the behavior of the LTPP-FHWA test sections is made through a factorial programming. Analysis of Variance (ANOVA) allows the determination of the level of significance of pre-selected factors (independent variables: traffic, age and pavement structure) and the development of performance prediction models (dependent variables: roughness and rutting).

In 2007, the Mechanistic-Empirical Pavement Design Guide (MEPDG) was successfully approved as the new American Association of State Highway and Transportation Officials (AASHTO) pavement design standard (Von Quintus et al., 2007). Calibration and validation of the MEPDG is currently in progress in several provinces across Canada. The MEPDG will be used as the standard pavement design methodology for the foreseeable future (Tighe, 2013). This new pavement design process requires several parameters specific to local conditions of the design location. In order to perform an accurate analysis, a database of parameters including those specific to local materials, climate and traffic are required to calibrate the models in the MEPDG. In 1989, the Canadian Strategic Highway Research Program (C-SHRP) launched a national full scale field experiment known as the Canadian Long-Term Pavement Performance (C-LTPP) program. Between the years, 1989 and 1992, a total of 24 test sites were constructed within all ten provinces. Each test site contained multiple monitored sections for a total of 65 sections. Each of these sites received rehabilitation treatments of various thicknesses of asphalt overlays. The C-LTPP program attempted to design and build the test sections across Canada so as to cover the widest range of experimental factors such as traffic loading, environmental region, and subgrade type. With planned strategic pavement data collection cycles, it would then be possible to compare results obtained at different test sites (i.e. across traffic levels, environmental zones, soil types) across the country. The United States Long-Term Pavement Performance (US-LTPP) database is serving as a critical tool in implementing the new design guide. The MEPDG was delivered with the prediction models calibrated to average national conditions. For the guide to be an effective resource for individual agencies, the national models need to be evaluated against local and regional performance. The results of these evaluations are being used to determine if local calibration is required. It is expected that provincial agencies across Canada will use both C-LTPP and US-LTPP test sites for these evaluations. In addition, C-LTPP and US-LTPP sites provide typical values for many of the MEPDG inputs (C-SHRP, 2000). The scope of this thesis is to examine the existing data in the C-LTPP database and assess its relevance to Canadian MEPDG calibration. Specifically, the thesis examines the dynamic modulus parameter (|E*|) and how it can be computed using existing C-LTPP data and an Artificial Neural Network (ANN) model developed under a Federal Highway Administration (FHWA) study (FHWA, 2011). The dynamic modulus is an essential property that defines the stiffness characteristics of a Hot Mix Asphalt (HMA) mixture as a function of both its temperature and rate of loading. |E*| is also a primary material property input required for a Level 1 analysis in the MEPDG. In order to perform a Level 1 MEPDG analysis, detailed local material, environmental and traffic parameters are required for the pavement section being analyzed. Additionally, it can be used in various pavement response models based on visco-elasticity. The dynamic modulus values predicted using both Level 2 and Level 3 viscosity-based ANN models in the ANNACAP software showed a good correlation to the measured dynamic modulus values for two C-LTPP test sections and supplementary Ontario mixes. These findings support previous research findings done during the development of the ANN models. The viscosity-based prediction model requires the least amount data in order to run a prediction. A Level 2 analysis requires mix volumetric data as well as viscosity testing and a Level 3 analysis only requires the PG grade of the binder used in the HMA. The ANN models can be used as an alternative to the MEPDG default predictions (Level 3 analysis) and to develop the master curves and determine the parameters needed for a Level 1 MEPDG analysis. In summary, Both the Level 2 and Level 3 viscosity-based model results demonstrated strong correlations to measured values indicating that either would be a suitable alternative to dynamic modulus laboratory testing. The new MEPDG design methodology is the future of pavement design and research in North America. Current MEPDG analysis practices across the country use default inputs for the dynamic modulus. However, dynamic modulus laboratory characterization of asphalt mixes across Canada is time consuming and not very cost-effective. This thesis has shown that Level 2 and Level 3 viscosity-based ANN predictions can be used in order to perform a Level 1 MEPDG analysis. Further development and use of ANN models in dynamic modulus prediction has the potential to provide many benefits.

In 2007, the Mechanistic-Empirical Pavement Design Guide (MEPDG) was successfully approved as the new American Association of State Highway and Transportation Officials (AASHTO) pavement design standard (Von Quintus et al., 2007). Calibration and validation of the MEPDG is currently in progress in several provinces across Canada. The MEPDG will be used as the standard pavement design methodology for the foreseeable future (Tighe, 2013). This new pavement design process requires several parameters specific to local conditions of the design location. In order to perform an accurate analysis, a database of parameters including those specific to local materials, climate and traffic are required to calibrate the models in the MEPDG. In 1989, the Canadian Strategic Highway Research Program (C-SHRP) launched a national full scale field experiment known as the Canadian Long-Term Pavement Performance (C-LTPP) program. Between the years, 1989 and 1992, a total of 24 test sites were constructed within all ten provinces. Each test site contained multiple monitored sections for a total of 65 sections. Each of these sites received rehabilitation treatments of various thicknesses of asphalt overlays. The C-LTPP program attempted to design and build the test sections across Canada so as to cover the widest range of experimental factors such as traffic loading, environmental region, and subgrade type. With planned strategic pavement data collection cycles, it would then be possible to compare results obtained at different test sites (i.e. across traffic levels, environmental zones, soil types) across the country. The United States Long-Term Pavement Performance (US-LTPP) database is serving as a critical tool in implementing the new design guide. The MEPDG was delivered with the prediction models calibrated to average national conditions. For the guide to be an effective resource for individual agencies, the national models need to be evaluated against local and regional performance. The results of these evaluations are being used to determine if local calibration is required. It is expected that provincial agencies across Canada will use both C-LTPP and US-LTPP test sites for these evaluations. In addition, C-LTPP and US-LTPP sites provide typical values for many of the MEPDG inputs (C-SHRP, 2000). The scope of this thesis is to examine the existing data in the C-LTPP database and assess its relevance to Canadian MEPDG calibration. Specifically, the thesis examines the dynamic modulus parameter (|E*|) and how it can be computed using existing C-LTPP data and an Artificial Neural Network (ANN) model developed under a Federal Highway Administration (FHWA) study (FHWA, 2011). The dynamic modulus is an essential property that defines the stiffness characteristics of a Hot Mix Asphalt (HMA) mixture as a function of both its temperature and rate of loading. |E*| is also a primary material property input required for a Level 1 analysis in the MEPDG. In order to perform a Level 1 MEPDG analysis, detailed local material, environmental and traffic parameters are required for the pavement section being analyzed. Additionally, it can be used in various pavement response models based on visco-elasticity. The dynamic modulus values predicted using both Level 2 and Level 3 viscosity-based ANN models in the ANNACAP software showed a good correlation to the measured dynamic modulus values for two C-LTPP test sections and supplementary Ontario mixes. These findings support previous research findings done during the development of the ANN models. The viscosity-based prediction model requires the least amount data in order to run a prediction. A Level 2 analysis requires mix volumetric data as well as viscosity testing and a Level 3 analysis only requires the PG grade of the binder used in the HMA. The ANN models can be used as an alternative to the MEPDG default predictions (Level 3 analysis) and to develop the master curves and determine the parameters needed for a Level 1 MEPDG analysis. In summary, Both the Level 2 and Level 3 viscosity-based model results demonstrated strong correlations to measured values indicating that either would be a suitable alternative to dynamic modulus laboratory testing. The new MEPDG design methodology is the future of pavement design and research in North America. Current MEPDG analysis practices across the country use default inputs for the dynamic modulus. However, dynamic modulus laboratory characterization of asphalt mixes across Canada is time consuming and not very cost-effective. This thesis has shown that Level 2 and Level 3 viscosity-based ANN predictions can be used in order to perform a Level 1 MEPDG analysis. Further development and use of ANN models in dynamic modulus prediction has the potential to provide many benefits.

The roads in our society are in a state of constant degradation. The reasons for this are many, and therefore constructed to have a certain lifetime before being reconstructed. To minimize the cost of maintaining the important transport road network high quality prediction models are needed. This report presents new models for flexible pavement structures for initiation and propagation of fatigue cracks in the bound layers and rutting for the whole structure. The models are based on observations from the Swedish Long Term Pavement Performance (LTPP) database. The intention is to use them for planning maintenance as part of a pavement management system (PMS). A statistical approach is used for the modeling, where both cracking and rutting are related to traffic data, climate conditions, and the subgrade characteristics as well as the pavement structure. / <p>QC 20130325</p>

Os modelos de previsão de desempenho de pavimentos são importantes ferramentas utilizadas pelos sistemas de gerência, essenciais para o planejamento das atividades de manutenção e reabilitação, assim como para a estimativa dos recursos necessários para a preservação das rodovias. Este trabalho tem por objetivo comparar modelos de desempenho de pavimentos, desenvolvidos por análises empíricas e empírico-mecanísticas, que predizem a evolução da condição de pavimentos flexíveis, ao longo do tempo e/ou tráfego acumulado. Os modelos de desempenho analisados foram desenvolvidos por pesquisadores e órgãos rodoviários brasileiros e internacionais, inclusive os modelos de deterioração utilizados pelo programa computacional de gerência de pavimentos desenvolvido pelo Banco Mundial, o HDM–4 (Highway Development and Management). A pesquisa está baseada na comparação do desempenho real de seções de pavimentos rodoviários, obtido a partir da base de dados dos experimentos LTPP (Long-Term Pavement Performance) do FHWA (Federal Highway Administration), com o comportamento previsto pelos modelos de desempenho desenvolvidos por Queiroz (1981), Paterson (1987), Marcon (1996) e Yshiba (2003). Neste trabalho, a análise do comportamento das seções de teste LTPP-FHWA é feita utilizando-se uma programação fatorial que, através da análise de variância (ANOVA), permite a determinação do nível de significância de fatores pré- selecionados (variáveis independentes: tráfego, idade e número estrutural corrigido) bem como a modelagem do desempenho dos pavimentos dessas seções (variáveis dependentes: irregularidade longitudinal e deformação permanente). / The pavement performance prediction models are important tools used for pavement management, essential for the planning of maintenance and rehabilitation activities, as well as for budgeting. The aim of this work is to compare performance prediction models developed through empirical and empirical-mechanistic analyses, which predict the evolution of the condition of flexible pavements, throughout the time and/or accumulated traffic. The performance prediction models analyzed were developed by researchers and Brazilian and international road agencies, including the deterioration models used by the pavement management comuputer program HDM-4 (Highway Development and Management), developed by the World Bank. The research is based on the comparison of the real performance of pavement of sections, obtained from the data base of the LTPP Program (Long-Term Pavement Performance) of FHWA (Federal Highway Administration), with the behavior predicted by deterioration models developed by Queiroz (1981), Paterson (1987), Marcon (1996) and Yshiba (2003). In this work, the analysis of the behavior of the LTPP-FHWA test sections is made through a factorial programming. Analysis of Variance (ANOVA) allows the determination of the level of significance of pre-selected factors (independent variables: traffic, age and pavement structure) and the development of performance prediction models (dependent variables: roughness and rutting).

abstract: Several ways exist to improve pavement performance over time. One suggestion is to tailor the asphalt pavement mix design according to certain specified specifications, set up by each state agency. Another option suggests the addition of modifiers that are known to improve pavement performance, such as crumb rubber and fibers. Nowadays, improving asphalt pavement structures to meet specific climate conditions is a must. In addition, time and cost are two crucial settings and are very important to consider; these factors sometimes play a huge role in modifying the asphalt mix design needed to be set into place, and therefore alter the desired pavement performance over the expected life span of the structure. In recent studies, some methods refer to predicting pavement performance based on the asphalt mixtures volumetric properties. In this research, an effort was undertaken to gather and collect most recent asphalt mixtures’ design data and compare it to historical data such as those available in the Long-Term Pavement Performance (LTPP), maintained by the Federal Highway Administration (FHWA). The new asphalt mixture design data was collected from 25 states within the United States and separated according to the four suggested climatic regions. The previously designed asphalt mixture designs in the 1960’s present in the LTPP Database implemented for the test sections were compared with the recently designed pavement mixtures gathered, and pavement performance was assessed using predictive models. Three predictive models were studied in this research. The models were related to three major asphalt pavement distresses: Rutting, Fatigue Cracking and Thermal Cracking. Once the performance of the asphalt mixtures was assessed, four ranking criteria were developed to support the assessment of the mix designs quality at hand; namely, Low, Satisfactory, Good or Excellent. The evaluation results were reasonable and deemed acceptable. Out of the 48 asphalt mixtures design evaluated, the majority were between Satisfactory and Good. The evaluation methodology and criteria developed are helpful tools in determining the quality of asphalt mixtures produced by the different agencies. They provide a quick insight on the needed improvement/modification against the potential development of distress during the lifespan of the pavement structure. / Dissertation/Thesis / Masters Thesis Civil, Environmental and Sustainable Engineering 2020

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.

abstract: The accurate prediction of pavement network condition and performance is important for efficient management of the transportation infrastructure system. By reducing the error of the pavement deterioration prediction, agencies can save budgets significantly through timely intervention and accurate planning. The objective of this research study was to develop a methodology for calculating a pavement condition index (PCI) based on historical distress data collected in the databases from Long-Term Pavement Performance (LTPP) program and Minnesota Road Research (Mn/ROAD) project. Excel™ templates were developed and successfully used to import distress data from both databases and directly calculate PCIs for test sections. Pavement performance master curve construction and verification based on the PCIs were also developed as part of this research effort. The analysis and results of LTPP data for several case studies indicated that the study approach is rational and yielded good to excellent statistical measures of accuracy. It is believed that the InfoPaveTM LTPP and Mn/ROAD database can benefit from the PCI templates developed in this study, by making them available for users to compute PCIs for specific road sections of interest. In addition, the PCI-based performance model development can be also incorporated in future versions of InfoPaveTM. This study explored and analyzed asphalt pavement sections. However, the process can be also extended to Portland cement concrete test sections. State agencies are encouraged to implement similar analysis and modeling approach for their specific road distress data to validate the findings. / Dissertation/Thesis / Masters Thesis Civil Engineering 2015

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