Machine learning-driven seismic failure mode identification of reinforced concrete shear walls based on PCA feature extraction

Abstract

As the major lateral load-resisting components in building structures, reinforced concrete (RC) shear waolls exhibit a powerful capability of load-carrying as well as complicated mechanism under cyclic forces. Scenarios are there in regional risk assessment and post-earthquake renovation calling for stringent prerequisite of rapid failure mode identification. Great endeavor has been dedicated to utilize machine learning (ML) techniques to perform this task, while very few of them has paid adequate emphasis on exploratory data analysis or feature extraction during implementation. Hence, this study explored out a novel strategy by allocating principal component analysis (PCA) for experimental data mining and feature extraction, which was proved a powerful capability of enhancing the prediction accuracy compared with training with raw data. Based on the assembled dataset consisting of 181 RC shear walls by this study, nine classical and state-of-the-art ML algorithms were investigated, among which gradient boosting classifier achieved a relevantly high accuracy of 0.98 after performed with hyperparameter optimization. Besides, clustering patterns of experimental parameters in PCA analysis and feature importance in fitted model explainability were elaborated as well, which presented valuable information and consistent underlying mechanism. As the first attempt in RC shear walls scope to utilized PCA for feature extraction, this study achieved to rapidly predict the failure modes in an extremely satisfying accuracy, which presented obvious superiority compared with prevalent operation, indicating a powerful capability and vast feasibility in similar task implementation.