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Este trabalho foi realizado com o objetivo de aplicar o Modelo de Tavakoli, que é um modelo de gerência de pavimentos para vias urbanas, para análise de priorização de vias urbanas em cidade de médio porte. Para isso se utilizou um estudo de campo previamente realizado pela Prefeitura Municipal de Suzano e pelo Laboratório de Mecânica de Pavimentos da Universidade de São Paulo no município de Suzano, no ano de 2007, com o qual foi criado um banco de dados das vias do município. Naquele estudo foi empregado o modelo HDM-4 do Banco Mundial para a priorização de intervenções de manutenção nas vias públicas. No presente trabalho buscou-se elaborar um estudo comparativo entre aquela solução e a solução atingida pelo modelo de Tavakoli para um mesmo conjunto de vias. Para este trabalho o Modelo de Tavakoli foi adaptado para planilhas eletrônicas e aplicado em um conjunto de seções de pavimentos, permitindo a determinação de uma lista de prioridades de intervenções. Posteriormente, essas intervenções foram simuladas para o mesmo conjunto de vias, buscando a priorização dos serviços, com o modelo HDM-4 versão 1.0. Através deste trabalho foi possível concluir que os dois critérios estudados podem ser úteis para gerenciar vias urbanas, pois priorizaram as intervenções necessárias nas vias urbanas analisadas. Foi possível verificar que o Modelo de Tavakoli se revelou mais conservador, por ser um critério fechado, com um índice de prioridades dependendo diretamente do índice da condição do pavimento e do volume diário médio de tráfego, afetando diretamente a classificação das seções. No Modelo HDM-4 a classificação das seções e os valores de irregularidade não tiveram uma sincronia, pois o HDM-4 é um modelo de critérios abertos, que permite ao usuário acrescentar vários parâmetros, e com estes foi possível reproduzir uma situação um pouco mais próxima da realidade. / This study was conceived in order to apply the Tavakoli Model, which is a pavement management system for urban roads, aiming the analysis of prioritization of urban streets in a mid-sized city. A field study previously conducted by Suzano City and the Pavements Mechanics Laboratory of the University of São Paulo in Suzano City in 2007 was used, from which was created a database of the towns streets. In that study, the Highway Design and Management (HDM-4) Model of the World Bank was employed for prioritizing maintenance work on public roads. In this study it was sought to develop a comparative study between the mentioned solution and the one reached by Tavakolis Model in the same set of roads. In this research the Tavakoli Model was adapted for the spreadsheets and applied to a set of pavement sections, allowing the determination of a priority list of interventions including the specific type of intervention for each segment. Subsequently, these same types of strategies or interventions were simulated for the same set of paths, seeking the prioritization, with HDM-4 Model version 1.0. Through this research it was possible to conclude that both criteria studied can be useful for managing urban roads, because they prioritized the necessary interventions in the urban roads analyzed. The Tavakoli Model proved to be more conservative, on account of being a closed criterion, with a priority index depending directly on the pavement condition index and average daily volume of traffic, affecting the classification of the sections directly as well. In the HDM-4 Model, the classification of the sections and values of the International Roughness Index did not match, due to HDM-4 being an open criteria model, which allows the user to add several parameters, and they can lead to more realistic report.

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

Este trabalho foi realizado com o objetivo de aplicar o Modelo de Tavakoli, que é um modelo de gerência de pavimentos para vias urbanas, para análise de priorização de vias urbanas em cidade de médio porte. Para isso se utilizou um estudo de campo previamente realizado pela Prefeitura Municipal de Suzano e pelo Laboratório de Mecânica de Pavimentos da Universidade de São Paulo no município de Suzano, no ano de 2007, com o qual foi criado um banco de dados das vias do município. Naquele estudo foi empregado o modelo HDM-4 do Banco Mundial para a priorização de intervenções de manutenção nas vias públicas. No presente trabalho buscou-se elaborar um estudo comparativo entre aquela solução e a solução atingida pelo modelo de Tavakoli para um mesmo conjunto de vias. Para este trabalho o Modelo de Tavakoli foi adaptado para planilhas eletrônicas e aplicado em um conjunto de seções de pavimentos, permitindo a determinação de uma lista de prioridades de intervenções. Posteriormente, essas intervenções foram simuladas para o mesmo conjunto de vias, buscando a priorização dos serviços, com o modelo HDM-4 versão 1.0. Através deste trabalho foi possível concluir que os dois critérios estudados podem ser úteis para gerenciar vias urbanas, pois priorizaram as intervenções necessárias nas vias urbanas analisadas. Foi possível verificar que o Modelo de Tavakoli se revelou mais conservador, por ser um critério fechado, com um índice de prioridades dependendo diretamente do índice da condição do pavimento e do volume diário médio de tráfego, afetando diretamente a classificação das seções. No Modelo HDM-4 a classificação das seções e os valores de irregularidade não tiveram uma sincronia, pois o HDM-4 é um modelo de critérios abertos, que permite ao usuário acrescentar vários parâmetros, e com estes foi possível reproduzir uma situação um pouco mais próxima da realidade. / This study was conceived in order to apply the Tavakoli Model, which is a pavement management system for urban roads, aiming the analysis of prioritization of urban streets in a mid-sized city. A field study previously conducted by Suzano City and the Pavements Mechanics Laboratory of the University of São Paulo in Suzano City in 2007 was used, from which was created a database of the towns streets. In that study, the Highway Design and Management (HDM-4) Model of the World Bank was employed for prioritizing maintenance work on public roads. In this study it was sought to develop a comparative study between the mentioned solution and the one reached by Tavakolis Model in the same set of roads. In this research the Tavakoli Model was adapted for the spreadsheets and applied to a set of pavement sections, allowing the determination of a priority list of interventions including the specific type of intervention for each segment. Subsequently, these same types of strategies or interventions were simulated for the same set of paths, seeking the prioritization, with HDM-4 Model version 1.0. Through this research it was possible to conclude that both criteria studied can be useful for managing urban roads, because they prioritized the necessary interventions in the urban roads analyzed. The Tavakoli Model proved to be more conservative, on account of being a closed criterion, with a priority index depending directly on the pavement condition index and average daily volume of traffic, affecting the classification of the sections directly as well. In the HDM-4 Model, the classification of the sections and values of the International Roughness Index did not match, due to HDM-4 being an open criteria model, which allows the user to add several parameters, and they can lead to more realistic report.

With the growing need to maintain roadway systems for provision of safety and comfort for travelers, network level decision-making becomes more vital than ever. In order to keep pace with this fast evolving trend, highway authorities must maintain extremely effective databases to keep track of their highway maintenance needs. Florida Department of Transportation (FDOT), as a leader in transportation innovations in the U.S., maintains Pavement Condition Survey (PCS) database of cracking, rutting, and ride information that are updated annually. Crack rating is an important parameter used by FDOT for making maintenance decisions and budget appropriation. By establishing a crack rating threshold below which traveler comfort is not assured, authorities can screen the pavement sections which are in need of Maintenance and Rehabilitation (M&R). Hence, accurate and reliable prediction of crack thresholds is essential to optimize the rehabilitation budget and manpower. Transition Probability Matrices (TPM) can be utilized to accurately predict the deterioration of crack ratings leading to the threshold. Such TPMs are usually developed by historical data or expert or experienced maintenance engineers' opinion. When historical data are used to develop TPMs, deterioration trends have been used vii indiscriminately, i.e. with no discrimination made between pavements that degrade at different rates. However, a more discriminatory method is used in this thesis to develop TPMs based on classifying pavements first into two groups. They are pavements with relatively high traffic and, pavements with a history of excessive degradation due to delayed rehabilitation. The new approach uses a multiple non-linear regression process to separately optimize TPMs for the two groups selected by prior screening of the database. The developed TPMs are shown to have minimal prediction errors with respect to crack ratings in the database that were not used in the TPM formation. It is concluded that the above two groups are statistically different from each other with respect to the rate of cracking. The observed significant differences in the deterioration trends would provide a valuable tool for the authorities in making critical network-level decisions. The same methodology can be applied in other transportation agencies based on the corresponding databases.

Previous studies show that pavement condition properties are significant factors to enhance road safety and riding experience, and pavements with low quality might have inadequate performance in terms of safety and riding experience. Pavement Management System (PMS) databases include pavement properties for each segment of the road collected by the agencies. Understanding the impact of road characteristics on crash frequency is a key step to prevent crashes. Whereas other studies analyzed the effect of different characteristics such as International Roughness Index (IRI), Rutting Depth (RD), Annual Average Daily Traffic (AADT), this thesis analyzed the effect of Critical Condition Index (CCI) on crash frequency, in addition to the other factors identified in previous studies. Other characteristics such as Percentage of Heavy Vehicles, Road Surface Condition, Road Lighting Condition, and Driver Conditions are taken into the consideration. The scope of the study is the interstate highway system in Fairfax County, Virginia. Negative Binomial, Least Square and Nominal Logistic Models were developed, showing that the CCI value is a significant factor to predict the number of crashes, and that it has different effect for different values of AADT. The result of this study is a substantial step towards developing an integrated transportation control and infrastructure management framework. / Master of Science / Many factors cause crashes in the roads. Although there is a common sense that road characteristics such as asphalt quality are important in terms of road safety, there are few studies that scientifically prove that statement. In addition, asphalt maintenance decisions making process is mainly based on cost benefit optimization, and traffic safety is not considered at the process. The purpose of this study is to analyze crashes and road characteristics related to each crash to understand the effect of those characteristics on crash frequency, and eventually, to build a model to predict the number of crashes at each part of the road. The model can help transportation agencies to have a better understanding in terms of safety consequences of their infrastructure management plans. The scope of this study is the highway interstate system in Northern Virginia. Results suggest that pavement condition has a significant impact on crash frequency.