Failure mode detection of reinforced concrete shear walls using ensemble deep neural networks

Abstract

Reinforced concrete structural walls (RCSWs) are one of the most efficient lateral force-resisting systems used in buildings, providing sufficient strength, stiffness, and deformation capacities to withstand the forces generated during earthquake ground motions. Identifying the failure mode of the RCSWs is a critical task that can assist engineers and designers in choosing appropriate retrofitting solutions. This study evaluates the efficiency of three ensemble deep neural network models, including the model averaging ensemble, weighted average ensemble, and integrated stacking ensemble for predicting the failure mode of the RCSWs. The ensemble deep neural network models are compared against previous studies that used traditional well-known ensemble models (AdaBoost, XGBoost, LightGBM, CatBoost) and traditional machine learning methods (Naive Bayes, K-Nearest Neighbors, Decision Tree, and Random Forest). The weighted average ensemble model is proposed as the best-suited prediction model for identifying the failure mode since it has the highest accuracy, precision, and recall among the alternative models. In addition, since complex and advanced machine learning-based models are commonly referred to as black-box, the SHapley Additive exPlanation method is also used to interpret the model workflow and illustrate the importance and contribution of the components that impact determining the failure mode of the RCSWs.