Neural Network Modeling of Slabs Under Simultaneous Aircraft and Temperature Loading

This study focuses on the development and performance of a comprehensive artificial neural network (ANN) model for the analysis of jointed concrete slabs under simultaneous aircraft and temperature loading. Using the results of the ILLI-SLAB finite element program, a comprehensive artificial neural network model was trained for the different loading conditions of gear loading only, temperature loading only, and simultaneous aircraft and temperature loading cases. Comparing the ANN predictions to the ILLI-SLAB solutions validated the ANN model. The trained ANN model gave maximum bending stresses and maximum vertical deflections within an average absolute error of 1.4 percent of those obtained directly from ILLI-SLAB analyses. The typical ANN prediction time is about 0.3 million times faster than the average ILLI-SLAB finite element solution. Therefore, the use of an ANN-based design tool is deemed to be very effective for studying hundreds or thousands of “what if” scenarios for including the temperature effects in pavement design.