Neural network-based seismic response prediction model for building structures using artificial earthquakes

In this paper, a new model for predicting seismic responses of buildings based on the correlation of ground motion (GM) and the structure is presented by simulating numerous artificial earthquakes (AEQs). In the model, neural network (NN) configurations representing the relationships between GM characteristics and seismic responses of a structure are developed to predict responses of the structure with only GM data measured by monitoring system in future seismic events. To extract the GM characteristics, multiple AEQs corresponding to the design response spectrum are generated based on probabilistic vibration theory, instead of using historical earthquakes. In the presented NN configurations, GM characteristics including mean and predominant period, significant duration, and peak ground acceleration are established as the input layer and the maximum interstory drift ratio and maximum displacement are established as the output layer. In addition, a new parameter called resonance area is proposed to represent the relationship between a GM and a target structure in the frequency domain and utilized in the NN input layer. By employing the new parameter, dynamic characteristics of the structure are considered in the response estimation of the model with related to GM. The model is applied to seismic response prediction for four multi-degrees-of-freedom (MDOF) structures with different natural periods using 2700 AEQs. The validities of the presented NN models are confirmed by investigating the performance of response prediction. The effectiveness of the resonance area parameter in the NN for predicting the seismic responses is assessed and discussed. Furthermore, the effects of the constitution of NNs and computational costs of those NNs on estimation were investigated. Finally, the presented model is employed for prediction of seismic responses for a structural model of a planar reinforced concrete building structure. (C) 2019 Published by Elsevier Ltd.