The primary goal of this research was to show that artificial neural network (ANN) models could be developed to perform rapid and accurate predictions of jointed plain concrete pavement system (JPCP) parameters which will enable pavement engineers to incorporate the state-of-the-art finite element (FE) solutions into routine practical design. The ISLAB2000 finite element program has been used as an advanced structural model for solving the responses of the concrete pavement systems and generating a large knowledge database.;Totally, fifty-six ANN-based backcalculation and forward calculation models were developed as part of this research for the analysis of JPCP systems under traffic and temperature loading combinations to predict the concrete pavement parameters and critical pavement responses. In this research, BCM stands for the ANN-based backcalculation model and FCM stands for the ANN-based forward calculation model. BCM-EPCC, BCM-kS, BCMTELTD, FCM-RRS, and FCM-sigma MAX models were developed for the prediction of elastic modulus of Portland cement concrete (PCC) layer (EPCC), coefficient of subgrade reaction (kS) of the pavement foundation, total effective linear temperature difference (TELTD) between top and bottom of the PCC layer, radius of relative stiffness (RRS) of the pavement system, and maximum tensile stresses at the bottom of the Portland cement concrete layer (sigmaMAX), respectively. These ANN-based models gave average errors less than 1% for synthetic database. In order to develop more robust networks that can tolerate the noisy or inaccurate pavement deflection patterns collected from the Falling Weight Deflectometer (FWD) field tests, several network architectures were also trained with varying levels of noise in them.;One of the most important advantages of the presented ANN approach is that the use of the ANN-based models resulted in a drastic reduction in computation time. Rapid prediction ability of the ANN-based models (capable of analyzing 100,000 FWD deflection profiles in one second) provides a tremendous advantage to the pavement engineers by allowing them to nondestructively assess the condition of the transportation infrastructure in real time while the FWD testing takes place in the field. In the developed approach, there is also no need a seed moduli or iteration process of the solution in order to predict the JPCP system parameters. The prediction of temperature difference (TELTD) in PCC layer which causes the slab curling and warping in concrete pavements is another tremendous advantage of the developed approach over the other methods since no other method does not take into account this parameter in the analyses. Finally, it can be concluded that ANN-based analysis models can provide pavement engineers and designers with state-of-the-art solutions, without the need for a high degree of expertise in the input and output of the problem, to rapidly analyze a large number of concrete pavement deflection basins needed for project specific and network level pavement testing and evaluation.