SCNet: A Multi-attribute Data Set for Seismic Collapse Behavior of Deep Steel Wide-Flange Columns

In this project, we explore the efficiency of different machine learning (ML) methods in predicting the seismic collapse behavior of steel deep wide flange (W-shape) columns. Steel Column Net (SCNet), a database of more than nine hundred deep W-shape columns subjected to combined axial and lateral loads is collected and compiled. The efficiency of five ML classification models is explored to identify the failure modes of columns in a randomly assigned test set from SCNet. Whereas, the efficiency of four ML regression models is explored to predict the cumulative inelastic rotation of columns in a randomly assigned test set from SCNet.