{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T13:18:35Z","timestamp":1785417515968,"version":"3.56.0"},"reference-count":26,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T00:00:00Z","timestamp":1783123200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100017316","name":"Public Works Research Institute","doi-asserted-by":"crossref","award":["JPJ012187"],"award-info":[{"award-number":["JPJ012187"]}],"id":[{"id":"10.13039\/100017316","id-type":"DOI","asserted-by":"crossref"}]},{"award":["JPJ012187"],"award-info":[{"award-number":["JPJ012187"]}],"id":[{"id":"https:\/\/ror.org\/04b8j4a33","id-type":"ROR","asserted-by":"publisher"}]},{"name":"JSPS Grant-in-Aid for Scientific Research","award":["25K01302"],"award-info":[{"award-number":["25K01302"]}]},{"name":"JSPS Grant-in-Aid for Scientific Research","award":["25K22110"],"award-info":[{"award-number":["25K22110"]}]},{"name":"JSPS Grant-in-Aid for Scientific Research","award":["23H00198"],"award-info":[{"award-number":["23H00198"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>As a pilot-scale feasibility study, aging bridge infrastructure and limited inspection resources have created an urgent need for automated and reliable bridge condition assessment systems. Most existing deep learning-based inspection approaches detect damage types from images without considering the structural member on which the damage occurs, limiting their practical utility for maintenance decision-making. This study proposes a structure-aware deep learning framework for automated bridge inspection that integrates structural member segmentation, two-class damage detection, and spatial damage-to-member association within a unified pipeline. A SegFormer-based semantic segmentation model was trained on a custom bridge inspection dataset comprising 1339 images to identify three primary structural member classes\u2014main girder, deck slab, and abutment\u2014achieving a test mean Intersection over Union (mIoU) of 0.851. Boundary refinement using the Segment Anything Model (SAM) in mask-prompt mode was applied to improve mask precision during training data preparation. A YOLOv8s object detection model was trained on a custom bridge damage dataset of 9142 annotated images (6531 training, 1740 validation, and 871 test images) to detect two damage classes\u2014crack and corrosion\u2014achieving a mean Average Precision (mAP50) of 0.445 at a confidence threshold of 0.30. The framework associates detected damage with segmented structural members using a region-based spatial assignment strategy, enabling structure-aware outputs such as \u201ccrack on main girder\u201d and \u201ccorrosion on deck slab.\u201d Manual evaluation on 100 bridge inspection images demonstrated a fully correct damage detection accuracy of 70.0% and a fully correct member assignment accuracy of 62.0%. When partially correct predictions were additionally considered for qualitative analysis, the corresponding accuracies increased to 84.0% and 87.0%, respectively. The main girder class achieved the highest combined accuracy for both damage detection (90.9%) and member assignment (93.9%). These results demonstrate the potential of the proposed framework as a first layer for AI-assisted bridge inspection by associating detected damage with structural members, providing structured inspection information to support subsequent maintenance assessment and infrastructure monitoring.<\/jats:p>","DOI":"10.3390\/s26134255","type":"journal-article","created":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T12:01:37Z","timestamp":1783339297000},"page":"4255","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Structure-Aware Deep Learning Framework for Automated Bridge Inspection Integrating SegFormer-Based Structural Member Segmentation and YOLOv8 Damage Detection"],"prefix":"10.3390","volume":"26","author":[{"given":"Sushama","family":"De Silva","sequence":"first","affiliation":[{"name":"Institute of Engineering Innovation, School of Engineering, The University of Tokyo, Tokyo 113-8656, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9755-8435","authenticated-orcid":false,"given":"Pang-jo","family":"Chun","sequence":"additional","affiliation":[{"name":"Institute of Engineering Innovation, School of Engineering, The University of Tokyo, Tokyo 113-8656, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,4]]},"reference":[{"key":"ref_1","unstructured":"American Society of Civil Engineers (2025). 2025 Infrastructure Report Card: Bridges, ASCE. 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