Prediction of seismic drift responses of planar steel moment frames using artificial neural network and extreme gradient boosting
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- Prediction of seismic drift responses of planar steel moment frames using artificial neural network and extreme gradient boosting
- Nguyen, Hoang D.; Dao, Nhan D.; Shin, Myoungsu
- Issue Date
- Pergamon Press Ltd.
- ENGINEERING STRUCTURES, v.242, pp.112518
- This study aims to develop machine learning (ML) models that can predict the seismic responses of planar steel moment-resisting frames subjected to ground motions. For this purpose, two of the most powerful ML techniques, artificial neural network (ANN) and extreme gradient boosting (XGBoost), were applied. To generate a comprehensive dataset for the training and testing of the ML models, 22,464 nonlinear dynamic analyses were conducted on 36 steel moment frames with different structural characteristics (i.e., number of stories, number of bays, column-to-beam moment capacity ratio) subjected to 624 ground motions. The selected ground motions had peak accelerations greater than 0.2 g and earthquake magnitudes of at least 4.0. The maximum top and interstory drifts were considered as the primary seismic responses of the steel frames. The ANN and XGBoost models were developed using MATLAB and XGBoost 1.1.1 Package, respectively. The results suggest that both the ANN and XGBoost models could reliably estimate the seismic drift responses of the steel frames. The XGBoost model achieved a better prediction than the ANN model in all the considered cases. The coefficients of determination ( of the XGBoost model for the testing dataset are 0.975 and 0.962 in the maximum top and interstory drifts, respectively. The significance of the input variables on the prediction of the seismic drift responses was also analyzed. The ground motion intensities (e.g., peak ground acceleration, velocity, displacement) had a more significant impact on the seismic drift response prediction compared with the earthquake (e.g., magnitude) and soil characteristics, and the spectral accelerations in the first three natural periods. In particular, the peak ground velocity had the most significant effect on the seismic drift responses prediction. Finally, a graphical user interface based on the XGBoost model was developed for the preliminary estimation of the seismic drift responses of steel moment frames.
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