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For one/two-semester, undergraduate/graduate courses in Pavement Design. This up-to-date text covers both theoretical and practical aspects of pavement analysis and design. It includes some of the latest developments in the field, and some very useful computer software-developed by the author-with detailed instructions.
Several important issues concerning the effect of slab support on concrete pavement performance were studied in a National Cooperative Highway Research Program project, "Support Under Concrete Pavements" (NCHRP 1-30). The results were promising, however, the data that were available for analysis were limited. This study was conducted to further field-verify and develop the improved support guidelines proposed in NCHRP 1-30, using the Long-Term Pavement Performance (LTPP) database, in order to establish their practicality and appropriateness for use in concrete pavement design nationwide. This study was also conducted to further field-verify the proposed revised American Association of State Highway and Transportation Officials (AASHTO) performance model to the fullest extent possible using the design, materials, climate, traffic, and performance data available in the LTPP database for General Pavement Section 3 (GPS-3) (jointed plain concrete pavement), GPS-4 (jointed reinforced concrete pavement), and GPS-5 (continuously reinforced concrete pavement). Guidelines, revised on the basis of the results of this field verification study, are presented in the appendix in the form of a proposed addendum to the AASHTO Design Guide. The documentation of these field verification efforts using the LTPP database is provided in this report.
Fatigue cracking and reflection cracking criteria were developed and incorporated into a design procedure for flexible and rigid overlays of rigid pavements. Linear elastic layered theory is the basic model used to compute stresses and strains in the pavement system, which are then input to the fatigue and reflection cracking models. The fatigue criteria developments resulted from analyses of the performance of rigid pavements at the AASHO Road Test.
The significant contribution of the research presented in this report lies in the fact that it utilizes the most comprehensive database of pavement conditions that is readily available and promises to provide the sought data in future years. The first part of this report reviews the existing literature covering related topics including pavement roughness, the LLTP background, artificial neural networks, regression analysis and the existing pavement deterioration models. The second part discusses the work done in data analysis and data manipulation in addition to the development of the training of the neural network model. The third part deals with various aspects of the model development using neural networks and regression analysis. The next part concludes the research with summarizing the results of model development. The models developed in this research are then compared to some existing models by applying the models to similar data sets and performing statistical analysis of the results.