Analytical model based on artificial neural network for masonry shear walls strengthened with FRM systems

Abstract

Abstract In the future Fiber Reinforced Polymer (FRP) materials will be considered a common strengthening system for civil structures thanks to several research efforts made in the last decades. For those applications in which FRP materials show some restrictions such as, above all, the incompatibility with heritage buildings, a new generation of fibrous materials has been developed. Researchers have investigated Fiber Reinforced Mortars (FRM) as external structural and seismic reinforcement. One of the most attractive applications of these materials is related to the in-plane shear strength of masonry walls. In this scenario, an analytical model based on Artificial Neural Network (ANN) is proposed and discussed in respect of the geometrical and mechanical variables that control the mechanical problem. An ANN is presented in the paper by showing its possible productive application in the civil engineering field. The proposed model seems able to predict the shear strength of FRM strengthened masonry; the approach is considered efficient since it includes both a theoretical method and a large test calibration, illustrated herein. Thanks to a quite small input database of laboratory results, ANN seems able to provide a theoretical solution to the problem with accuracy and precision.