Selection of the structural severest design ground motions based on big data and random forest

Abstract

When calculating the time history of the seismic response, the conventional approach only processes a limited number of ground motions because of the elevated computational costs associated with nonlinear time history analysis and the lack of useable earthquake data. To solve this problem, a selection method for the severest design ground motions based on big data and Random Forest is proposed. Firstly, to address the issue of insufficient data, the Stanford Earthquake Dataset including over one million ground motions, was obtained and processed into the data format that can be used in earthquake engineering. Then, a structural response prediction algorithm based on Random Forest is suggested to substitute the nonlinear time history analysis, and compared to Support Vector Machine and Extreme Gradient Boosting. Finally, under all ground motions in the Stanford Earthquake Dataset, the proposed model is used to predict the structural response, include a reinforcement concrete isolated continuous girder bridge and a reinforcement concrete six-layer frame. The severest design ground motions for both structures were determined based on ranked calculated structural response. The conclusions of this study are as follows: The processed dataset provides a solution for implementing intelligent algorithms in the field of seismic engineering; Random Forest optimized through Bayesian optimization performs well in predicting structural demand measures, demonstrating superior generalization performance with R2 of 0.918 and root mean square error of 0.214, compared to other models. For actual civil structures, the proposed method can determine the structural severest ground motions at two different fortification standards.