Failure mode identification of column base plate connection using data-driven machine learning techniques

Column base plate (CBP) connection is one of the most important structural elements of steel structures since the failure of these base plate connections can result in the collapse of the entire structure. The prediction of failure mode of CBP connection plays a significant role in ductile behavior of the structure which is critical for damage assessment or retrofitting strategies after any natural hazard. This study introduces a rapid failure mode identification technique for CBP connections by exploring the recent advances in machine learning (ML) techniques. A comprehensive database is assembled with 189 available experimental results for CBP connections including various parameters affecting the CBP behavior. To establish the best classification model, a total of nine different ML algorithms such as Support vector machine, Naive bayes, K-nearest neighbors, Decision tree, Random forest, Adaboost, XGboost, LightGBM, and Catboost are considered in this study. Comparing the developed ML models, the Decision tree based ML model is suggested in this study which has an overall accuracy of 91% for identifying the failure mode of CBP connections. It is also found that base plate thickness, embedment length, and anchor rod diameter are the influential parameters that govern the failure mode of CBP connections. Furthermore, an opensource classification model is provided to rapidly identify the failure mode of CBP connection by allowing modifications for future developments.