Data-driven machine-learning-based seismic failure mode identification of reinforced concrete shear walls

Abstract

A reinforced concrete shear wall is one of the most critical structural members in buildings, in terms of carrying lateral loads. Despite its importance, post-earthquake reconnaissance and recent experimental studies have highlighted the insufficient safety margins of shear walls. The lack of empirical and mechanics-based models prevents rapid failure mode identification of existing shear walls. This study builds on recent advances in the area of machine learning to determine the failure mode of shear walls as a function of geometric configurations, material properties, and reinforcement details. This study assembles a comprehensive database consisting of 393 experimental results for shear walls with various geometric configurations. Eight machine learning models, including Naïve Bayes, K-Nearest Neighbors, Decision Tree, Random Forest, AdaBoost, XGBoost, LightGBM, and CatBoost were evaluated in this study, in order to establish the best prediction model. As a result of detailed evaluation, a machine learning model based on the Random Forest method is proposed in this paper. The proposed method has 86% accuracy in identifying the failure mode of shear walls. This study also demonstrates that aspect ratio, boundary element reinforcement indices, and wall length-to-wall thickness ratio are the critical parameters influencing the failure mode of shear walls. Finally, an open-source data-driven classification model that can be used in design offices across the world is provided in this paper. The proposed model has the flexibility to account for additional experimental results yielding new insights.