@Article{sdhm.2025.071148,
AUTHOR = {Xinrui Ma, Shizhi Chen},
TITLE = {STPEIC: A Swin Transformer-Based Framework for Interpretable Post-Earthquake Structural Classification},
JOURNAL = {Structural Durability \& Health Monitoring},
VOLUME = {19},
YEAR = {2025},
NUMBER = {6},
PAGES = {1745--1767},
URL = {http://www.techscience.com/sdhm/v19n6/64516},
ISSN = {1930-2991},
ABSTRACT = {The rapid and accurate assessment of structural damage following an earthquake is crucial for effective emergency response and post-disaster recovery. Traditional manual inspection methods are often slow, labor-intensive, and prone to human error. To address these challenges, this study proposes STPEIC (Swin Transformer-based Framework for Interpretable Post-Earthquake Structural Classification), an automated deep learning framework designed for analyzing post-earthquake images. STPEIC performs two key tasks: structural components classification and damage level classification. By leveraging the hierarchical attention mechanisms of the Swin Transformer (Shifted Window Transformer), the model achieves 85.4% accuracy in structural component classification and 85.1% accuracy in damage level classification. To enhance model interpretability, visual explanation heatmaps are incorporated, highlighting semantically relevant regions that the model uses for decision-making. These heatmaps closely align with real-world structural and damage features, confirming that STPEIC learns meaningful representations rather than relying on spurious correlations. Additionally, a graphical user interface (GUI) has been developed to streamline image input, classification, and interpretability visualization, improving the practical usability of the system. Overall, STPEIC provides a reliable, interpretable, and user-friendly solution for rapid post-earthquake structural evaluation.},
DOI = {10.32604/sdhm.2025.071148}
}