Wan, W., Zhang, J., Chen, G., Yang, X., & Cui, F. (2026). Attention-Enhanced Deep Learning Framework for Automated Concrete Crack Depth Prediction Using Infrared Thermography. International Journal of Computational Methods. https://doi.org/10.1142/s0219876226500234
Abstract:
Traditional manual methods for measuring concrete crack depth are inefficient, time-consuming, and heavily reliant on operator experience, often resulting in inconsistent and subjective outcomes. Moreover, most existing studies on crack characterization primarily emphasize surface-level parameters such as crack length, width, and area. The crack depth, a key indicator of structural integrity and residual load-bearing capacity, remains insufficiently addressed. To bridge this gap, this study proposes an automated crack depth prediction framework that integrates infrared thermography (IRT) with an enhanced SE-ResNet-18 deep learning model. Concrete beam specimens with precisely calibrated crack depths were fabricated under controlled laboratory conditions, and corresponding thermal images were acquired to establish a robust training dataset. By embedding a squeeze-and-excitation (SE) attention mechanism into the conventional ResNet-18 architecture, the model’s capacity to capture and emphasize salient thermal features was significantly improved, resulting in more accurate and stable depth predictions. Experimental results demonstrate that the proposed SE-ResNet-18 achieves 93.77% accuracy within a ±1[Formula: see text]mm tolerance, outperforming the baseline ResNet-18 network by a substantial margin. This solution is fully automated in its predictive analysis and noncontact in its sensing modality. It shows strong potential for practical implementation in real-world structural health monitoring and provides a foundation for future research on field-scale applications and model generalization under varying environmental conditions.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the A*STAR - Battery Remanufacturing for Improved Circular Ecosystems’.
Grant Reference no. : M24N2a0076