Machine-learning for damage assessment of rubble stone masonry piers based on crack patterns

Under seismic actions, stone masonry buildings are prone to damage. To assess the severity of damaged masonry buildings and their failure modes, engineers connect these problems to surface crack features, such as the crack width and the extent of cracking. We aim to further these assessments in this study, wherein we propose using simple machine learning models to predict: 1) three ratios encoding the degradation of stiffness, strength, and displacement capacity of damaged rubble stone masonry piers as a function of the observed crack features and the applied axial load and shear span ratio; and 2) the pre-peak vs. post-peak regime, based on the crack features.