Failure mode and effects analysis of RC members based on machine-learning-based SHapley Additive exPlanations (SHAP) approach

Abstract

Machine learning approaches can establish the complex and non-linear relationship among input and response variables for the seismic damage assessment of structures. However, lack of explainability of complex machine learning models prevents their use in such assessment. This paper uses extensive experimental databases to suggest random forest machine learning models for failure mode predictions of reinforced concrete columns and shear walls, employs the recently developed SHapley Additive exPlanations approach to rank input variables for identification of failure modes, and explains why the machine learning model predicts a specific failure mode for a given sample or experiment. A random forest model established provides an accuracy of 84% and 86% for unknown data of columns and shear walls, respectively. The geometric variables and reinforcement indices are critical parameters that influence failure modes. The study also reveals that existing strategies of failure mode identification based solely on geometric features are not enough to properly identify failure modes.