Artificial neural networks as design tools in concrete airfield pavement design

An artificial neural network (ANN) model has been trained in this study with the results of ILLT-SLAB finite element program and used as an analysis design tool for predicting stresses in jointed concrete airfield pavements. In addition to various load locations (slab interior, corners and/or edges) and joint load transfer efficiencies, a wide range of realistic airfield slab thicknesses and subgrade supports were considered in training of the ANN model. Under identical dual wheel type loading conditions, the trained ANN model produces stresses within an average of 0.38 percent of those obtained from finite element analyses. The trained ANN model has been found to be very effective for correctly predicting ILLI-SLAB stresses, practically in the blink of an eye, with no requirements of complicated finite element inputs. The ANN model is currently being expanded to handle several other aircraft gear configurations and multiple wheel loading conditions. Design curves created from these neural network models will eventually enable pavement engineers to easily incorporate current sophisticated state-of-the-art technology into routine practical design.