Neural Network Approximation of the Masonry Failure under Biaxial Compressive Stress

Abstract eng:
Masonry is a material that exhibits distinct directional properties because the mortar joints act as planes of weakness. To define failure under biaxial stress, a threedimensional surface in terms of the two normal stresses and shear stress, or the two principal stresses and their orientation to the bed joints, is required. Researchers have long been aware of the significance of the bed joint angle to the applied load and many experimental tests have been carried out on brick masonry discs to produce indirect tensile stresses on joints inclined at various angles to the vertical compressive load. The highest strength of the masonry has been observed for the cases when the compressive load was perpendicular to the bed joints or when the principal tensile stress at the center of the disc was parallel to the bed joints. In this case failure occurred through bricks and perpendicular joints. The lowest strength has been observed when the compressive load was parallel to the bed joints or when the principal tensile stress at the center of the disc was perpendicular to the bed joints. In this case failure occurred along the interface of brick and mortar joint. In the present study, the preliminary results of an ongoing research on the failure of brittle anisotropic materials are presented. In particular, Neural Networks (NNs) are used in order to approximate the experimental failure curves of a brittle anisotropic material such as masonry, that has been investigated in depth by Page [1]. For each angle θ (0º, 22.5º, 45º), a Neural Network is trained with the experimental data of Page as inputs. Then the NN is asked to produce the whole failure curve for each angle as its output, filling also the gaps between the experimental points with appropriate approximations. The results show the great potential of using NN for the approximation of the masonry failure under biaxial compressive stress.

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