Predicting the seismic response of the short structures by considering the Whale Optimization Algorithm

Abstract

This paper presents a newly improved version of the Whale Optimization Algorithm based on quantum theory by optimizing the Artificial Neural Network for the ground response approximation problems in short buildings. Based on the literature, Artificial Neural Network does not provide a proper response of ground surface prediction in seismic load applications. Hence, the main idea of this study is to assess the utilization of the whale optimization algorithm for calculating the columns’ horizontal deflection in the short structure under notable considered seismic loading. The input database utilized in this paper is the Chi-Chi earthquake that is occurred in 1999 in Taiwan. To provide the train and test databases for the artificial neural network algorithm and whale optimization algorithm, Finite Element models are used. The inputs contain the Chi-Chi earthquake’s dynamic time, soil elastic modulus, dilation angle, Poisson’s ratio, unit weight, friction angle, bending stiffness and axial stiffness. Also the columns’ horizontal deflection in their highest level is considered as the output. The results showed the higher reliability of the proposed WOA in calculating the ground response and the column’s horizontal deflection in short buildings subjected to the earthquake loading.