Prediction of national airport pavement test facility pavement layer moduli from heavy weight deflectometer test data using artificial neural networks.

The National Airport Pavement Test Facility (NAPTF) was constructed to generate full-scale testing data to investigate the performance of airport pavements subjected to complex gear loading configurations of new generation aircraft. During the first test program, the NAPTF test sections were simultaneously subjected to Boeing 777 trafficking in one lane and Boeing 747 trafficking in another lane using the National Airport Pavement Test Machine. To monitor the effect of time and traffic on pavement structural responses, heavy weight deflectometer (HWD) tests were conducted on the trafficked lanes and the untrafficked centerline of flexible test sections as trafficking progressed. The primary objective of this study was to develop a tool for backcalculating NAPTF non-linear flexible pavement layer moduli from HWD data using artificial neural networks (ANN). A multi-layer, feedforward network that uses an error-backpropagation algorithm was trained to approximate the HWD backcalculation function. The synthetic database generated using the non-linear pavement finite element program, ILLI-PAVE, was used to train the ANN. Using the ANN, the asphalt concrete moduli and subgrade moduli were successfully predicted. Further research is required to develop ANN models for predicting the granular layer moduli. These results could be used to compare the relative effect of Boeing 777 and Boeing 747 trafficking on the elastic moduli and characterize the seasonal variation in moduli values. The same concept could also be used for backcalculating non-linear pavement moduli of highway pavements for input into mechanistic-empirical analysis and design.