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While understanding the shear strength of stone masonry structures is important for the design and the mainte- nance, we still lack computational tools for predicting the strength as a function of the stone layout. Here we implement an end-to-end image based kinematic analysis framework that converts the image of a stone layout of a wall into a 2D kinematic model. Machine learning and image processing techniques are applied to convert a wall image into a rigid block model, which is then used as the geometry input for an existing limit analysis approach using mathematical pro- gramming. This existing approach is extended such that also cohesion, limited tensile and compressive strength can be considered in the point-based formulation of interface failure. We apply the method to simulate the strength of stone masonry walls with mortar that are subjected to shear-compression loading and show that our method can demonstrate the influence of the stone masonry typology on the shear strength.
Lyesse Laloui, Alessio Ferrari, Eleonora Crisci
Mário Alexandre De Jesus Garrido, Mateus De Assunção Hofmann