Prediction of the response of masonry walls under blast loading using Artificial Neural Networks

Abstract

A methodology to predict key aspects of the structural response of masonry walls under blast loading using artificial neural networks (ANN) is presented in this paper. Failure patterns of masonry walls due to in and out of plane loading are complex, due to potential opening and sliding of the mortar joint interfaces between the masonry stones. To capture this response, advanced computational models can be developed requiring significant amount of resources and computational effort. The article uses an advanced non-linear finite element model to capture the failure response of masonry walls under blast loads, introducing unilateral contact-friction laws between stones and damage mechanics laws for the stones. Parametric finite simulations are automatically conducted, using commercial finite element software linked with MATLAB and Python. A dataset is then created and used to train an artificial neural network. The trained neural network is able to predict the out of plane response of the masonry wall for random properties of the blast load (standoff distance and weight). Results indicate that the accuracy of the proposed framework is satisfactory. Comparison of the computational time needed for a single finite element simulation and for a prediction of the out of plane response of the wall by the trained neural network highlights the benefits of the proposed machine learning approach in terms of computational time and resources. Therefore, the proposed approach can be used to substitute time consuming explicit dynamic finite element simulations and used as a reliable tool in the fast prediction of the masonry response under blast actions.