Deep Learning-based Post-earthquake Structural Damage Classification with Small Datasets
Conference paper
Abeysuriya, K., Ciupala, A., Manikonda, M., Jamalpuram, K., Sonar, A. N., Sharif, S., Ghorashi, S. and Ilki, A. 2025. Deep Learning-based Post-earthquake Structural Damage Classification with Small Datasets. 7th International Conference on Imaging, Vision and Patter Recognition (IVPR).
Authors | Abeysuriya, K., Ciupala, A., Manikonda, M., Jamalpuram, K., Sonar, A. N., Sharif, S., Ghorashi, S. and Ilki, A. |
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Type | Conference paper |
Abstract | This paper introduces a deep learning-based approach for automated classification of local structural element failure modes in post-earthquake buildings using image-based data. Addressing the critical challenge of limited training datasets in the structural/earthquake engineering domain, targeted, domain-informed data augmentation and synthetic data generation techniques are proposed to enhance dataset size and diversity. The model architecture and preprocessing pipeline are explicitly designed to capture damage-sensitive features in images that are essential for informed decision-making on structural integrity of the building, thus extending beyond conventional classification tasks. Dataset enhancement, transfer learning and model regularisation techniques are integrated to ensure alignment of model predictions with expert domain judgement. Achieving 0.93 (93%) accuracy, precision, recall and F1-score, the developed model exhibits robust generalisability without overfitting, demonstrating clear potential for practical deployment in disaster resilience and infrastructure recovery efforts. |
Year | 2025 |
Conference | 7th International Conference on Imaging, Vision and Patter Recognition (IVPR) |
12th International Conference on Informatics, Electronics and Vision (ICIEV) | |
Accepted author manuscript | License File Access Level Anyone |
Publication process dates | |
Submitted | 30 Apr 2025 |
Completed | 29 May 2025 |
Deposited | 08 Jul 2025 |
Journal | International Journal of Computer Vision and Signal Processing |
Journal citation | p. In press |
ISSN | 2186-1390 |
Web address (URL) | https://cennser.org/IJCVSP/index.html |
Copyright holder | © 2025 The Authors |
Additional information | Presented at combined conference: IVPRICIEV2025 (Kitakyushu, Japan), 26 -29 May 2025 |
https://repository.uel.ac.uk/item/8zwzz
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Accepted author manuscript
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