Gradient transformer Self-Attention U-Net for enhanced crack detection in concrete bridges.

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Accurate identification of cracks in bridge components is a critical task, yet traditional inspection methods often fall short due to their limitations in accuracy and efficiency. This paper introduces the Gradient Transformer Attention U-Net (GTAU-Net) model, a novel deep learning approach that significantly advances crack detection in bridge components. Unlike conventional attention-based U-Nets, GTAU-Net introduces a Quantum Fused Filter (QFF) to pre-process images by integrating multiple edge and gradient patterns through a quantum-inspired hybrid filtering strategy. It further computes Gradient Saliency Scores (GSS) to dynamically guide the self-attention mechanism, enabling more precise localization and feature extraction. Through this dual enhancement, GTAU-Net effectively handles varying crack sizes, shapes, orientations, and environmental conditions. Experimental results demonstrate that GTAU-Net achieves an impressive 99.42% accuracy significantly outperforming existing models and measures crack lengths, highlighting sustainability. This research contributes to the advancement of automated crack detection technology, offering a promising solution for enhancing infrastructure safety and durability. To promote transparency and reproducibility, the code and dataset used in this study are publicly available at Zenodo: https://doi.org/10.5281/zenodo.15617661 .", "claims": [{"id": "claim-method", "statement": "To evaluate the effectiveness of the proposed GTAU-Net, statistical significance testing was conducted with paired t-tests using accuracy values from five independent runs for existing model along with the proposed model.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41131157", "locator": "sections[18] \"Statistical significance testing and performance validation\" paragraph 2", "quote": "To evaluate the effectiveness of the proposed GTAU-Net, statistical significance testing was conducted with paired t-tests using accuracy values from five independent runs for existing model along with the proposed model."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The experimental data that support for the results and findings of this paper are available on public repository from “Maguire, M., Dorafshan, S., & Thomas, R. 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