This report focuses on the impacts of traffic generated as a result of the windmill on pavement structures. The wind energy industry is a fast growing sector of the U.S. economy. Lately concerns have been raised over the transportation of heavy windmill components on the pavement infrastructure. This report analyzes the impacts of windmill traffic on two pavement structures in Texas: 1) rural interstate facility, and 2) rural collector roadway facility. Windmill traffic was disaggregated by windmill component and categorized into eight vehicle classes. Two traffic scenarios were developed and a damage ratio for pavement rutting was developed. Based on the rutting damage ratio, results showed that windmill traffic has a significant impact on rural collector facilities when compared against normal truck traffic activity. Meanwhile, impacts on rural interstate facilities were determined to be insignificant when compared to normal truck traffic activity. / text

The Texas Department of Transportation (TxDOT) is concerned about the widening gap between preservation needs and available funding. Funding levels are not adequate to meet the preservation needs of the roadway network; therefore projects listed in the 4-Year Pavement Management Plan must be ranked to determine which projects should be funded now and which can be postponed until a later year. Currently, each district uses locally developed methods to rank and prioritize projects. These ranking methods have relied on less formal qualitative assessments based on engineers’ subjective judgment. It is important for TxDOT to have a rational 4-Year Pavement Management Plan. The objective of this study is to develop a conceptual framework that describes the development of the 4-Year Pavement Management Plan and a proposed ranking process. It can be largely divided into three steps; (1) Network-Level preliminary project screening process, (2) Project-Level project ranking process, and (3) Economic Analysis. A rational pavement management procedure and a project ranking method that are accepted by districts and the TxDOT administration will maximize efficiency in budget allocations and help improve pavement condition. As a part of this study, based on the data provided by the Austin District Pavement Engineer, the Network-Level Project Screening (NLPS) tool, including the candidate project selection algorithm and the preliminary project screening matrix, is developed. The NLSP tool has been used by the Austin District Pavement Engineer (DPE) to evaluate the PMIS (Pavement Management Information System) data and to prepare a preliminary list of candidate projects for further evaluation. The automated tool will help TxDOT engineers easily incorporate the developed mathematical algorithm into their daily pavement maintenance management. / text

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.

This thesis presents the results of developing road pavement deterioration models for the State Highway network in New Zealand pavement deterioration models are an integral part of pavement management systems, which are used to forecast long-term maintenance needs and funding requirements on a road network. As part of this research, a Long-term Pavement Performance (LTPP) programme has been established on 63 sections of the State Highways. These sections are representative of typical road sections and climatic conditions on New Zealand roads. Data collection on these sections is undertaken on an annual basis and consists of high accuracy manual measurements. These measurements include road roughness, rutting, visual defect identification and strength testing with a Falling Weight Deflectometer. Based on the LTPP data, new model formats for New Zealand conditions were developed including a crack initiation model and a three-stage rut progression model. The rut progression model consists of three stages, initial densification, stable rut growth and a probabilistic model to predict accelerated rut progression. The continuous probabilistic model developed predicts the initiation of pavement failure events such as crack initiation and accelerated rutting. It has been found that this model type has a strong agreement with actual pavement behaviour as it recognises a distribution of failure on roads rather than failure occurring at an particular point in time, namely, a year. The modelling of rut progression in the three stages including, initial densification, stable rut progression and accelerated rutting has resulted in a significant increased understanding of this defect, especially for thin flexible chip seal pavements. It has been established that the in-service performance of these pavements is relatively predictable. However, incorporating both the in-service performance and the failure of pavements into one model was unrealistic. Therefore, by having the different stages of rutting, resulted into a more accurate forecasting of this defect. Although this research has covered the two priority pavement models including cracking and rutting prediction, it has established the model framework for other pavement models to be developed. As more data become available, further work can be undertaken to refine the models and to extend the research into the performance of alternative construction materials.

This thesis presents the results of developing road pavement deterioration models for the State Highway network in New Zealand pavement deterioration models are an integral part of pavement management systems, which are used to forecast long-term maintenance needs and funding requirements on a road network. As part of this research, a Long-term Pavement Performance (LTPP) programme has been established on 63 sections of the State Highways. These sections are representative of typical road sections and climatic conditions on New Zealand roads. Data collection on these sections is undertaken on an annual basis and consists of high accuracy manual measurements. These measurements include road roughness, rutting, visual defect identification and strength testing with a Falling Weight Deflectometer. Based on the LTPP data, new model formats for New Zealand conditions were developed including a crack initiation model and a three-stage rut progression model. The rut progression model consists of three stages, initial densification, stable rut growth and a probabilistic model to predict accelerated rut progression. The continuous probabilistic model developed predicts the initiation of pavement failure events such as crack initiation and accelerated rutting. It has been found that this model type has a strong agreement with actual pavement behaviour as it recognises a distribution of failure on roads rather than failure occurring at an particular point in time, namely, a year. The modelling of rut progression in the three stages including, initial densification, stable rut progression and accelerated rutting has resulted in a significant increased understanding of this defect, especially for thin flexible chip seal pavements. It has been established that the in-service performance of these pavements is relatively predictable. However, incorporating both the in-service performance and the failure of pavements into one model was unrealistic. Therefore, by having the different stages of rutting, resulted into a more accurate forecasting of this defect. Although this research has covered the two priority pavement models including cracking and rutting prediction, it has established the model framework for other pavement models to be developed. As more data become available, further work can be undertaken to refine the models and to extend the research into the performance of alternative construction materials.

This thesis presents the results of developing road pavement deterioration models for the State Highway network in New Zealand pavement deterioration models are an integral part of pavement management systems, which are used to forecast long-term maintenance needs and funding requirements on a road network. As part of this research, a Long-term Pavement Performance (LTPP) programme has been established on 63 sections of the State Highways. These sections are representative of typical road sections and climatic conditions on New Zealand roads. Data collection on these sections is undertaken on an annual basis and consists of high accuracy manual measurements. These measurements include road roughness, rutting, visual defect identification and strength testing with a Falling Weight Deflectometer. Based on the LTPP data, new model formats for New Zealand conditions were developed including a crack initiation model and a three-stage rut progression model. The rut progression model consists of three stages, initial densification, stable rut growth and a probabilistic model to predict accelerated rut progression. The continuous probabilistic model developed predicts the initiation of pavement failure events such as crack initiation and accelerated rutting. It has been found that this model type has a strong agreement with actual pavement behaviour as it recognises a distribution of failure on roads rather than failure occurring at an particular point in time, namely, a year. The modelling of rut progression in the three stages including, initial densification, stable rut progression and accelerated rutting has resulted in a significant increased understanding of this defect, especially for thin flexible chip seal pavements. It has been established that the in-service performance of these pavements is relatively predictable. However, incorporating both the in-service performance and the failure of pavements into one model was unrealistic. Therefore, by having the different stages of rutting, resulted into a more accurate forecasting of this defect. Although this research has covered the two priority pavement models including cracking and rutting prediction, it has established the model framework for other pavement models to be developed. As more data become available, further work can be undertaken to refine the models and to extend the research into the performance of alternative construction materials.

abstract: Pavement preservation is the practice of selecting and applying maintenance activities in order to extend pavement life, enhance performance, and ensure cost effectiveness. Pavement preservation methods should be applied before pavements display significant amounts of environmental distress. The long-term effectiveness of different pavement preservation techniques can be measured in terms of life extension, relative benefit, and benefit-cost ratio. Optimal timing of pavement preservation means that the given maintenance treatment is applied so that it will extend the life of the roadway for the longest possible period with the minimum cost. This document examines the effectiveness of chip seal treatment in four climatic zones in the United States. The Long-Term Pavement Performance database was used to extract roughness and traffic data, as well as the maintenance and rehabilitation histories of treated and untreated sections. The sections were categorized into smooth, medium, and rough pavements, based upon initial condition as indicated by the International Roughness Index. Pavement performance of treated and untreated sections was collectively modeled using exponential regression analysis. Effectiveness was evaluated in terms of life extension, relative benefit, and benefit-cost ratio. The results of the study verified the assumption that treated sections performed better than untreated sections. The results also showed that the life extension, relative benefit, and benefit cost ratio are highest for sections whose initial condition is smooth at the time of chip seal treatment. These same measures of effectiveness are lowest for pavements whose condition is rough at the time of treatment. Chip seal treatment effectiveness showed no correlation to climatic conditions or to traffic levels. / Dissertation/Thesis / M.S. Civil Engineering 2012

Preventive maintenance has the potential to improve network condition by retarding future pavement deterioration. The Virginia Department of Transportation uses its pavement management system to determine maintenance targets for each district. The districts then use these recommendations to select pavements that will receive maintenance and the types of treatments that will be applied. Each district has a different approach to preventive maintenance. There was a need for more consistent preventive maintenance practices across the state. This thesis outlines guidelines for the implementation of a preventive maintenance policy. Preventive maintenance treatments currently being used within Virginia include chip seal, slurry seal, microsurfacing, and thin hot mix asphalt overlays. Historical pavement condition data was obtained from the VDOT PMS for these treatments and treatment performance models were developed. A district level treatment selection tool was developed to assist the district level decision making process. A prioritized list of pavement sections was generated, maximizing the cost-effectiveness of the selected treatments subject to budgetary constraints set by the central office. The treatment selection tool was then run for each pavement classification in each district. The results of this analysis were presented. Although the recommended budget for each district was very close to the targets set by the central office, the recommended lane miles for each district were about half the targets set by the central office.  It is believed that the unit costs used in this analysis were higher than those used in the VDOT PMS analysis. This selection tool has the potential to be a very powerful decision support tool if the unit costs are representative of what the expected treatment costs are for each district. / Master of Science

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