Reinforced concrete (RC) walls are one of the most critical structural members in buildings to carry lateral loadings. Despite its importance, recent experimental studies and earthquake reconnaissance have highlighted the insufficient safety margins of shear walls. The lack of mechanics and empirical models prevents the rapid failure mode identification of exisiting shear walls. To this extend, this work assembles a comprehensive database consisting of 393 experimental results of various geometric configurations. Eight machine learning models such as Naïve Bayes, K-Nearest Neighbors, Decision Tree, Random Forest, ADABOOST, XGBOOST, and Light GBM, and Cat BOOST are explored in this study to establish the best prediction model
Detailed reference:
Mangalathu, S., Jang, H., Hwang, S-H., , Jeon, J-S., "Data-driven Machine Learning based Seismic Failure Mode Identification of Reinforced Concrete Shear Walls", Engineering Structures (https://doi.org/10.1016/j.engstruct.2020.110331)