Artificial Neural Networks for Predicting the Response of Unbonded Concrete Overlays in a Faulting Prediction Model

Project Details
STATE

PA

SOURCE

TRID

END DATE

06/04/19

RESEARCHERS

John DeSantis, Julie Vandenbossche, Steven Sachs

KEYWORDS

Composite pavements, Concrete overlays, Concrete pavements, Fault location, Neural networks, Pavement distress, Structural analysis, Transverse joints

Project description

Transverse joint faulting is a common distress in unbonded concrete overlays (UBOLs). However, the current faulting model in Pavement mechanistic-empirical (ME) is not suitable for accurately predicting the response of UBOLs. Therefore, to develop a more accurate faulting prediction model for UBOLs, the first step was to develop a predictive model that would be able to predict the response (deflections) of these structures. To account for the conditions unique to UBOLs, a computational model was developed using the pavement-specific finite element program ISLAB, to predict the response of these structures. The model was validated using falling weight deflectometer (FWD) data from existing field sections at the Minnesota Road Research Facility (MnROAD) as well as sections in Michigan. A factorial design was performed using ISLAB to efficiently populate a database of fictitious UBOLs and their responses.
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