Open Access

ARTICLE

STPEIC: A Swin Transformer-Based Framework for Interpretable Post-Earthquake Structural Classification

Xinrui Ma, Shizhi Chen^*

School of Highway, Chang’an University, Xi’an, 710064, China

* Corresponding Author: Shizhi Chen. Email:

(This article belongs to the Special Issue: Resilient and Sustainable Infrastructure: Monitoring, Safety, and Durability)

Structural Durability & Health Monitoring 2025, 19(6), 1745-1767. https://doi.org/10.32604/sdhm.2025.071148

Received 01 August 2025; Accepted 26 August 2025; Issue published 17 November 2025

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.

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Keywords

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