Prediction of seismic performance of steel frame structures: A machine learning approach

Abstract

The study of seismic performance in structural engineering is deemed crucial due to the intricate and unpredictable nature of earthquakes. This study explores advanced seismic performance prediction in structural engineering using machine learning techniques. Non-Linear Dynamic Analysis (NDA) was conducted on Steel Moment-Resisting Frames (SMRFs) situated on soil type D in seismic zone II with varying configurations using ETABS software. A substantial dataset comprising 29,200 data points from 292 models was generated to train machine learning models aimed at predicting the Maximum Inter-Story Drift Ratio (M-IDR), a critical parameter for assessing seismic limit-state capacity. The machine learning models, including Random Forest (RF), Extreme Gradient Boosting Machine (XGBoost), and Artificial Neural Networks (ANN), demonstrated high accuracy with R2 of 0.9625, 0.95327, and 0.94247 respectively, indicating a robust correlation between predicted and actual values. These results imply that the trained models can effectively predict seismic performance with high precision. A user-friendly Graphical User Interface (GUI) was developed using the trained models to facilitate the practical application of these models, significantly reducing computational costs and analytical efforts for researchers and engineers. The findings underscore the potential of integrating machine learning with structural engineering to enhance seismic performance predictions, contributing to the development of safer and more resilient structures.