Response estimation of reinforced concrete shear walls using artificial neural network and simulated annealing algorithm

Abstract

Reinforced concrete (RC) shear walls constitute a lateral load resisting system which is utilized in medium-rise and high-rise buildings. Recent earthquakes and corresponding studies have highlighted the importance of understanding the performance of reinforced concrete shear walls. Hence, there is an essential need to estimate the response of RC shear walls. However, predicting the response of the RC walls is difficult in this form due to the complex nature of the problem. In this research, a hybrid technique, the artificial neural network (ANN) and Simulated Annealing (SA), is considered to solve such complicated problems. (SA) algorithm is utilized to determine the optimal number of neurons of ANN and the percentage of data that should be used in the training and testing set. 14 learning algorithms are used and their performance is compared. The database, which is utilized in this study, consists of 150 RC shear walls. The coefficient of determination (R2) and root mean square error (RMSE) values of the best ANN model (ANN with Conjugate gradient backpropagation with Fletcher-Reeves updates learning algorithm) were found to be 0.97 and 1.66, respectively, which demonstrate a high capability of the SA-ANN algorithm in predicting RC shear walls’ responses. View PDF

Machine learning, Concrete shear wall, Simulated annealing, Artificial neural network, Critical parameters