This paper focuses on the analyses of jointed concrete slabs under the simultaneous temperature gradient and Boeing 777 aircraft gear loadings. Using the results from the ILLI-SLAB finite element program, a comprehensive artificial neural network (ANN) model was trained for the different conditions of aircraft loading only, temperature loading only, and the simultaneous aircraft and temperature loading cases. Special consideration has been given to the loading of a typical jointed slab assembly under the tri-tandem type gear of the B-777 aircraft. In addition to various slab load locations (interior, corners, and two mid-span slab edges) and joint load transfer efficiencies, a wide range of realistic airfield slab thicknesses and subgrade supports have been considered as ANN input conditions. More than 5,600 ILLI-SLAB analyses have provided the design parameters and the pavement responses as inputs for training the ANN model. Comparing the ANN predictions to the ILLI-SLAB solutions validated the performance of 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 the ILLI-SLAB analyses. A typical ANN run is about 0.3 million times faster than the ILLI-SLAB finite element solution. 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. A sample design is presented to illustrate how such an ANN-based design methodology can easily be applied in the pavement design process.