Automated Detection of Concrete Spalling in Post-Earthquake Structures Using Deep Learning

Post-earthquake structural assessment is critical in determining the extent of damage and guiding emergency response efforts. Spalling, characterized by the detachment of concrete layers, serves as a key indicator of seismic damage and can significantly impact structural integrity. This study develops an automated classification model utilizing deep learning to distinguish between spalling and non-spalling cases in concrete structures. The proposed method employs transfer learning with ResNet50 and EfficientNet-B3 to optimize accuracy and inference efficiency. The dataset, collected from real-world post-earthquake reconnaissance, consists of high-resolution images categorized into spalling and non-spalling classes. Key preprocessing techniques, including pixel normalization, data augmentation, and class balancing, were applied to improve model robustness and mitigate class imbalance issues. Performance evaluation showed that ResNet50 outperforms EfficientNet-B3 in overall accuracy (77% vs. 71%), while EfficientNet-B3 achieved higher recall (90% vs. 85%), making it more sensitive to detecting spalling cases. The study highlights the challenges posed by dataset variability and proposes future enhancements such as advanced augmentation, multi-modal data integration, and self-supervised learning. The findings contribute to the advancement of AI-driven structural health monitoring, offering an efficient tool for rapid post-disaster damage assessment