Ballast is one of the most commonly used construction materials in railway tracks. Under heavy train loads, ballast is subjected to a high stress level that is always associated with significant track deformation. Consequently, an accurate prediction of the mechanical behavior of ballast under static and dynamic loading conditions is important for the stability of railway tracks. In this paper, the feasibility of using artificial neural networks (ANNs) for modeling the mechanical behavior of railway ballast under static loading is investigated. The database used for the development of the ANN model is obtained from selected literature and comprises a series of 29 large-scale drained triaxial compression tests conducted on three types of commonly used ballast (i.e., basalt, dolomite, and granite). Predictions from the ANN model are compared with the results of experimental tests and with those obtained from the hardening-soil constitutive model in PLAXIS finite-element code. The results indicate that the ANN model is able to accurately predict the stress–strain and volume change behavior of ballast. The plastic dilation and contraction of ballast at various confining pressures and the strain-hardening and postpeak strain-softening behavior of ballast are also well simulated.Key words: ballast, modeling, neural networks, prediction, railway, triaxial tests.
