Kasthurirangan Gopalakrishnan and Marshall R. Thompson
The Heavy Weight Deflectometer (HWD) test is one of the most widely used tests for assessing the structural integrityof airport pavements in a non-destructive manner. The elastic moduli of the individual pavement layers “backcalculated” from the HWD deflection measurements are effective indicators of layer condition. Most of the backcalculation programs that are currently in use do not account for the non-linearity of unbound granular materials and fine-grained cohesive soils and therefore do not produce realistic results. The primary objective of this study was to develop a tool for backcalculating non-linear pavement layer moduli from HWD data using Artificial Neural Networks (ANN). A multi-layer, feed-forward network which uses an error-backpropagation algorithm was trained to approximate the HWD backcalculation function. The synthetic database generated using the nonlinear pavement finite-element program ILLI-PAVE was used to train the ANN. Using the ANN, we were successfully able to predict the AC moduli and subgrade moduli. The final product was used in backcalculatingp avement layer moduli from actual field data acquired at the National Airport Pavement Test Facility (NAPTF).