Abstract
Transverse joint faulting is a distress observed in bonded concrete overlays of asphalt pavements (BCOAs). However, to date, there is no predictive faulting model for BCOAs. Therefore, the objective of this research is to develop such a model. First, models were developed to predict the structural response of BCOAs due to environmental and traffic loads. Previously-developed artificial neural networks that rapidly estimate the structural response of BCOAs at the joint due to these loads was used to relate the structural response to the damage using the differential energy (DE) concept. Next, DE was related to faulting through an incremental analysis considering traffic, climate, and joint deterioration. Finally, a calibration using performance data from existing BCOAs throughout the continental United States and an extensive sensitivity analysis on the model’s prediction capabilities was performed. This faulting prediction model has been incorporated into the BCOA-ME design guide developed by the University of Pittsburgh.
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