Effect and Implications of Feature Selection on Machine Learning Prediction of Seismic Drift Demands and Collapse of Steel Moment Resisting Frames

Machine learning (ML) techniques were generated for different information levels to identify the minimum set of system parameters required for predicting collapse and maximum interstory drift (SDRmax) of steel moment resisting frame (SMRF) buildings. Five baseline modern SMRFs were evaluated under seismic loading with varying system and ground motion (GM) parameters to generate a database. Classification and regression-based ML models were tested at three system information levels to predict collapse and SDRmax, respectively. The ML predictions were mainly controlled by GM parameters and were relatively insensitive to system parameters defining nonlinear behavior, such as spring backbone curve features.