Application of Artificial Neural Network to Predict Load Bearing Capacity and Stiffness of Perforated Masonry Walls

Abstract

Perforations adversely affect the structural response of unreinforced masonry walls (UMW) by reducing the wall’s load bearing capacity, which can cause serious structural damage. In the absence of a reliable procedure to accurately predict the load bearing capacity and stiffness of perforated masonry walls subjected to in-plane loadings, this study presents a novel approach to measure these parameters by developing simple but practical equations. In this regard, the Multi-Pier (MP) method as a numerical approach was employed along with the application of an Artificial Neural Network (ANN). The simulated responses of centrally perforated UMW by the MP method were validated utilizing full-scale experimental walls. The validated MP model was used to generate a simulated database. The simulated database includes results of analyses for 49 different configurations of perforated masonry walls and their corresponding solid masonry walls. The effect of the area and shape of the perforations on the UMW’s behavior was evaluated by the MP method. Following the outcomes of the verified MP method, the ANN is trained to develop empirical equations to accurately predict the reduction in the load bearing capacity and initial stiffness due to the perforation of UMW. The results of this study indicate that the perforations have a significant effect on the structural capacity of the UMW subjected to in-plane loadings.

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