Machine learning-based prediction of hysteretic behaviors of two-side clamped steel shear walls

Abstract

This study proposes a methodology that can construct the hysteretic curves of two-side clamped steel shear walls (TCSSWs) using a machine learning technology to address the inherent deficiencies of multivariable regression analysis which has been typically used for developing the hysteretic model of a common structural element. A deep neural network (DNN) is adopted for this study and a new architecture is proposed by modifying numbers of neurons and hidden layers to well capture the cyclic response of TCSSWs. Using a training datasets DNN architectures with several combinations of input parameters and configurations relating to numbers of neurons and hidden layers had been trained. This study considers a total of 70 DNN architectures and selects the best-fitting DNN architecture which produces the minimum mean-square-error value. Estimates from the proposed DNN architecture are evaluated with a test dataset and their accuracy is acceptable. Finally, the selected DNN architecture is further verified with a validation dataset consisting of TCSSWs with different material properties of steel. The modeling parameters for the hysteretic curves of TCSSWs can be properly captured by the proposed DNN architecture.