Machine Learning-Based Methods for the Seismic Damage Classification of RC Buildings †

Abstract

This paper aims to investigate the feasibility of machine learning methods for the vulnerability assessment of buildings and structures. Traditionally, the seismic performance of buildings and structures is determined through a non-linear time–history analysis, which is an accurate but time-consuming process. As an alternative, structural responses of buildings under earthquakes can be obtained using well-trained machine learning models. In the current study, machine learning models for the damage classification of RC buildings are developed using the datasets generated from numerous incremental dynamic analyses. A variety of earthquake and structural parameters are considered as input parameters, while damage levels based on the maximum inter-story drift ratio are selected as the output. The performance and effectiveness of several machine learning algorithms, including ensemble methods and artificial neural networks, are investigated. The importance of different input parameters is studied. The results reveal that well-prepared machine learning models are also capable of predicting damage levels with an adequate level of accuracy and minimal computational effort. In this study, the XGBoost method generally outperforms the other algorithms, with the highest accuracy and generalizability. Simplified prediction models are also developed for preliminary estimation using the selected input parameters for practical usage.