This textbook lays out the state of the art for modeling of asphalt concrete as the major structural component of flexible pavements. The text adopts a pedagogy in which a scientific approach, based on materials science and continuum mechanics, predicts the performance of any configuration of flexible roadways subjected to cyclic loadings. The authors incorporate state-of the-art computational mechanics to predict the evolution of material properties, stresses and strains, and roadway deterioration. Designed specifically for both students and practitioners, the book presents fundamentally complex concepts in a clear and concise way that aids the roadway design community to assimilate the tools for designing sustainable roadways using both traditional and innovative technologies.

This report provides an appendix for the report with the reference: Perkins, S.W. (2001) Mechanistic Empirical Modeling and Design Model Development of Geosynthetic Reinforced Flexible Pavements: Final Report, Montana Department of Transportation, Helena, Montana, FHWA/MT 01 002/99160 1A, 156p. This report contains output from the software program DARWin for each design example provided in Appendix B of the above referenced report.

Design related project level pavement management - Economic evaluation of alternative pavement design strategies - Reliability / - Pavement design procedures for new construction or reconstruction : Design requirements - Highway pavement structural design - Low-volume road design / - Pavement design procedures for rehabilitation of existing pavements : Rehabilitation concepts - Guides for field data collection - Rehabilitation methods other than overlay - Rehabilitation methods with overlays / - Mechanistic-empirical design procedures.

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.

Pavement Design And Paving Material Selection are important for efficient, cost effective, durable, and safe transportation infrastructure Paving Materials and Pavement Analysis contains 73 papers examining bound and unbound material characterization, modeling, and performance of highway and airfield pavements. The papers in this publication were presented during the GeoShanghal 2010 International Conference held in Shanghai, China, June 3-5, 2010.

The Mechanistic Empirical Pavement Design Guide (MEPDG) method, currently known as Pavement ME, recommends using locally calibrated material characterization models developed from laboratory testing of local materials under specific environmental and traffic loading conditions. The Pavement ME design method offers a more realistic design procedure and reduces the uncertainty that arise from empirical design procedures. This thesis developed a locally calibrated indirect tensile (IDT) strength material model for low temperature cracking predictions of hot mix asphalt (HMA) in Manitoba, Canada. In addition, the research investigated the integration of locally calibrated HMA, and unbound granular material characterization models into the Pavement ME framework to improve the design of flexible pavements. Laboratory IDT testing was conducted on typical HMA mixtures containing extracted binders and varying percentages of reclaimed asphalt pavement (RAP). The laboratory measured IDT strengths were used to calibrate a local IDT strength predictive model for Manitoba. The predictions from the local Manitoba model were compared to the predictions from the global Pavement ME IDT model, and a Michigan calibrated IDT model, using a statistical analysis. It was found that the global Pavement ME IDT strength model, if used without local calibration, produced inaccurate predictions of the IDT strength for Manitoba mixtures. It was also found that binder characterization methods in Level 2 and Level 3 can significantly impact the accuracy of IDT strength predictions. A case study using developed local HMA, base, and subgrade material characterization models in Manitoba were compared to designs using default (Level 3) material input values in Pavement ME design software. The results of integrating the locally calibrated models for HMA, base and subgrade layers demonstrated that the locally calibrated materials model inputs produce lower pavement structural thicknesses with higher reliability in the predicted distresses when compared to the default materials inputs. The effect of using calibrated material inputs was more pronounced for higher traffic loadings. The results of the study demonstrate that the use of calibrated models can potentially produce optimized pavement thicknesses due to improved pavement designs.