Structural Damage Detection in Civil Infrastructures

Structural health monitoring (SHM) is a very significant component in maintaining the safety and durability of civil infrastructures. Manual inspection methods require a great deal of intensive labor, consume a lot of time and can be prone to human error. In this research work, we have presented a deep learning-based approach for automated crack detection in concrete surfaces using the SDNET2018 dataset. We have employed ResNet50 with transfer learning from ImageNet to classify images into cracked and non-cracked categories. The model achieves a test F1-score of 74.16% and a precision of 90.63%, indicating great detection performance. Grad-CAM visualizations have demonstrated that the network focuses on relevant regions of cracks, providing interpretability and confidence in predictions. The proposed approach shows promising potential to reduce manual inspection efforts and enable efficient, accurate structural health assessment. The code and the related resources of this research work will be made publicly available for verification and reproducibility purposes.