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

TL;DR

This paper presents a deep learning framework utilizing YOLO models to automatically detect and classify structural damage in concrete structures from post-earthquake images, enabling rapid damage assessment.

Contribution

It introduces a novel hybrid deep learning approach with fine-tuning and data augmentation for damage detection and classification in post-earthquake concrete structures.

Findings

Effective detection of exposed steel reinforcement and cracks.

High accuracy in damage level classification.

Rapid automated damage assessment demonstrated.

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.