Development of a Machine-Learning Method for Estimating Building Earthquake Damage Based on Pohang Earthquake Data

In the event of a large-scale earthquake, it is difficult to promptly estimate damage, making it challenging to establish an effective response strategy. To address these limitations, this study developed a preliminary earthquake damage assessment model for buildings using machine learning based on empirical damage data from the 2017 Pohang Earthquake. Six machine-learning models were established, and resampling techniques such as SMOTE (Synthetic Minority Oversampling Technique) and random sampling were applied for performance comparison and analysis to mitigate the chronic class imbalance problem of the dataset. The results indicate that the application of random sampling generally improves the model performance, with tree-based ensemble models achieving significantly high recall and AUC (Area Under the Curve) values. These findings suggest that the proposed model has a strong potential as an effective damage assessment tool for reliably detecting damaged buildings with minimal false negatives.