Abstract

This paper investigates the accuracy of a selection of expressions currently available to estimate the maximum shear strength of reinforced masonry walls. For this, the paper compares the prediction equations with a set of experimental results reported in the literature. The data set used include experimental information from specimens constructed with ceramic bricks and concrete blocks, on condition of partial and complete grouting, cyclically tested cantilever and double restrained with a clear shear failure. Based on the equations and analyzed the available experimental data, this research proposes alternative prediction to estimate the shear strength of reinforced masonry walls using artificial neural networks. The study result shows that the most accurate for prediction of the maximum shear strength expressions are proposed Matsumura (1987) and Tomazevic (1999), ceramic brick masonry and concrete block respectively. Also it demonstrated that artificial neural networks are reliable method for predicting the maximum shear resistance of reinforced masonry walls.