Mechanistic-Empirical Modelling of Flexible Pavement Performance Verifications Using APT Measurements
Abstract: Mechanistic-Empirical (M-E) pavement design procedures are composed of a reliable response model to estimate the state of stress in the pavement and distress models in order to predict the different types of pavement distresses due to the prevailing traffic and environmental conditions. One of the main objectives of this study was to develop a response model based on multilayer elastic theory (MLET) with improved computational performance by optimizing the time consuming parts of the MLET processes. A comprehensive comparison of the developed program with two widely used programs demonstrated excellent agreement and improved computational performance. Moreover, the program was extended to incorporate the viscoelastic behaviour of bituminous materials through elastic-viscoelastic correspondence principle. A procedure based on collocation of linear viscoelastic (LVE) solutions at selected key time durations was also proposed that improved the computational performance for LVE analysis of stationary and moving loads. A comparison of the LVE responses with measurements from accelerated pavement testing (APT) revealed a good agreement. Furthermore the developed response model was employed to evaluate permanent deformation models for bound and unbound granular materials (UGMs) using full scale APTs. The M-E Pavement Design Guide (MEPDG) model for UGMs and two relatively new models were evaluated to model the permanent deformation in UGMs. Moreover, for bound materials, the simplified form of the MEPDG model for bituminous bound layers was also evaluated. The measured and predicted permanent deformations were in general in good agreement, with only small discrepancies between the models. Finally, as heavy traffic loading is one of the main factors affecting the performance of flexible pavement, three types of characterizations for heavy traffic axle load spectrum for M-E analysis and design of pavement structures were evaluated. The study recommended an improved approach that enhanced the accuracy and computational performance.
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