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.
Acknowledgements
The authors would like to thank the Pennsylvania and California Departments of Transportation, especially Lydia Peddicord and Joshua Freeman of PennDOT, for some of the preliminary funding for this research. The authors would also like to thank the following people for their assistance in this research effort: John Harvey of the University of California – Davis and Angel Mateos University of California – Berkeley; Thomas Burnham and Dave Van Deusen at the MnROAD Research Facility for FWD, design information, and performance data; Stacy Lloyd of PennDOT, Tyson Rupnow and Xingwei Chen of LaDOT, Randall Riley formerly of the American Concrete Pavement Association, Bruce Bird, Amy Kohnert, and Ryan Peterson of IDOT, John Donahue of MoDOT, and Todd LaTorrella of the Missouri/Kansas Chapter American Concrete Pavement Association for data to populate the calibration database along with Linda Pierce of NCE for sharing performance data from NCHRP 1-61; and Charles Donnelly of the University of Pittsburgh.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Data availability statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.