Artificial Neural Networks for Analyzing Concrete Airfield Pavements Serving the Boeing B-777 Aircraft

Abstract

An artificial neural network (ANN) model has been trained with the results of the ILLI-SLAB finite element program and has been used to predict stresses and deflections in jointed concrete airfield pavements serving the Boeing B-777 aircraft. The trained ANN model produces stresses and deflections with average errors of less than 0.5 percent of those obtained directly from the finite element analyses. Use of the ANN model has been found to be very effective for correctly predicting ILLI-SLAB stresses and deflections, in less than 1 s, with no requirements of complicated finite element inputs. On the other hand, elastic layered programs (ELPs) currently are being used in mechanistic-based pavement design procedures for the analysis of jointed concrete pavements. Corrections are required to such ELP solutions to account for the effects of finite slab size, load location on the slab, and load transfer efficiencies of the joints. This can be accomplished by using the ANN model, which is being expanded to handle all possible aircraft gear configurations with multiple-wheel loading conditions by the use of the superposition principle. As demonstrated for the solution of the B-777 aircraft gear loadings, trained neural network models eventually will enable pavement engineers to easily incorporate current sophisticated state-of-the-art technology into routine practical design.