Modeling of masonry failure surface under biaxial compressive stress using Neural Networks

•Neural Networks are used in order to approximate the experimental results for masonry failure.•A two-step procedure is proposed, with the training of two types of Neural Networks.•The NNs showed great performance in fitting the experimental input data.•The curves generated by the NNs are continuous and smooth, but not necessarily convex.

Masonry is a brittle anisotropic material that exhibits distinct directional properties because the mortar joints act as planes of weakness. To define failure under biaxial stress, a 3D surface in terms of the two principal stresses and their orientation to the bed joints, is required. In the present study, a novel method is proposed on applying Neural Networks (NNs) to approximate the failure surface for such brittle anisotropic materials. The method comprises a series of NNs that are trained with available experimental data. The results demonstrate the great potential of using NNs for the approximation of masonry failure surface under biaxial compressive stress.