Seismic fragility analysis of steel moment frames using machine learning models

Abstract

This study develops machine learning (ML) models for seismic fragility analysis of steel moment frames. Four ML methods – random forest, adaptive boosting, gradient boosting regression tree (GBRT), and extreme gradient boosting (XGBoost) – were employed for this purpose. Probabilistic seismic demand models, each representing the relationship between the seismic response of a type of structure and ground motion intensity, were used to construct the fragility curves based on nonlinear time history analyses of 616 steel moment frames subjected to 240 ground motions. The first three natural periods of steel moment frames and a capacity limit state defined by the maximum interstory drift were selected as input variables for the ML models. Two parameters (median and logarithmic standard deviation) of a fragility function were considered as output variables for the ML models. For each steel frame, the capacity limit state values considered for maximum interstory drift cover a wide range to generalize the fragility curve outcomes. The interquartile range method was used to ensure the quality of the dataset, and consequently 56,479 data points were used for the development of ML models. Based on model performance, the GBRT (R2 = 0.9986, for the testing dataset) and XGBoost (R2 = 0.9987) models are proposed as the best models for fragility analysis of steel moment frames. Finally, a graphical user interface for fragility analysis of steel moment frames was built based on the two proposed models, for easy access by practicing engineers. This study demonstrates the applicability of ML methods in practical design.