Predicting Effective Parameters in Cyclic Behavior of Reinforced Masonry Walls with Shotcrete Using Artificial Neural Networks

Abstract

In Masonry buildings, walls are the main structural elements resistant to lateral loads. Reinforcing masonry walls by shotcreting one- or two-sides is one of the most common reinforcement methods. Considering the economic losses and casualties caused by damage to these elements in previous earthquakes, it is necessary to investigate the seismic behavior of these walls. The existing data can train artificial neural networks, and the behaviors could be generalized for future cases. In this study, the prediction of cyclic behavior parameters of reinforced masonry walls with one- and two-way shotcrete is investigated using different artificial neural network methods. Input parameters of the neural network include length, thickness, height, mortar shear strength, mortar compressive strength, mesh type, spring dimensions, rebar diameter, average thickness of shotcrete, and concrete compressive strength. Output parameters were the relative displacement of yield, relative displacement corresponding to maximum resistance, final relative displacement, yield strength, displacement resistance corresponding to maximum resistance, ultimate resistance, and initial stiffness. The results showed that the feed-forward back-propagation neural network could accurately predict the examined output parameters compared to other models, which can be considered as an alternative to some time-consuming and costly laboratory and analytical investigations.