Deep learning-based automated damage detection in concrete structures using images from earthquake events

• URL: http://arxiv.org/abs/2510.21063v1
• Date: Fri, 24 Oct 2025 00:35:14 GMT
• Title: Deep learning-based automated damage detection in concrete structures using images from earthquake events
• Authors: Abdullah Turer, Yongsheng Bai, Halil Sezen, Alper Yilmaz,
• Abstract summary: This study focuses on assessing the structural damage conditions using deep learning methods to detect exposed steel reinforcement in concrete buildings and bridges after large earthquakes.<n>New datasets of images collected after the 2023 Turkey Earthquakes were labeled to represent a wide variety of damaged concrete structures.<n>The proposed method builds upon a deep learning framework, enhanced with fine-tuning, data augmentation, and testing on public datasets.
• Score: 4.024850952459758
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Timely assessment of integrity of structures after seismic events is crucial for public safety and emergency response. This study focuses on assessing the structural damage conditions using deep learning methods to detect exposed steel reinforcement in concrete buildings and bridges after large earthquakes. Steel bars are typically exposed after concrete spalling or large flexural or shear cracks. The amount and distribution of exposed steel reinforcement is an indication of structural damage and degradation. To automatically detect exposed steel bars, new datasets of images collected after the 2023 Turkey Earthquakes were labeled to represent a wide variety of damaged concrete structures. The proposed method builds upon a deep learning framework, enhanced with fine-tuning, data augmentation, and testing on public datasets. An automated classification framework is developed that can be used to identify inside/outside buildings and structural components. Then, a YOLOv11 (You Only Look Once) model is trained to detect cracking and spalling damage and exposed bars. Another YOLO model is finetuned to distinguish different categories of structural damage levels. All these trained models are used to create a hybrid framework to automatically and reliably determine the damage levels from input images. This research demonstrates that rapid and automated damage detection following disasters is achievable across diverse damage contexts by utilizing image data collection, annotation, and deep learning approaches.

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