{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,24]],"date-time":"2026-01-24T17:32:56Z","timestamp":1769275976026,"version":"3.49.0"},"reference-count":81,"publisher":"American Society of Civil Engineers (ASCE)","issue":"3","content-domain":{"domain":["ascelibrary.org"],"crossmark-restriction":true},"short-container-title":["J. Comput. Civ. Eng."],"published-print":{"date-parts":[[2026,5]]},"DOI":"10.1061\/jccee5.cpeng-7247","type":"journal-article","created":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T08:58:36Z","timestamp":1769158716000},"update-policy":"https:\/\/doi.org\/10.1061\/do.news.20190416.0001","source":"Crossref","is-referenced-by-count":0,"title":["BSCS-Net: A Lightweight Segmentation Network for Automated Bridge Surface Crack Detection"],"prefix":"10.1061","volume":"40","author":[{"given":"Allen A.","family":"Zhang","sequence":"first","affiliation":[{"name":"Southwest Jiaotong Univ.","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dingfeng","family":"Wang","sequence":"additional","affiliation":[{"name":"Southwest Jiaotong Univ.","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"Peng","sequence":"additional","affiliation":[{"name":"Southwest Jiaotong Univ.","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huixuan","family":"Cheng","sequence":"additional","affiliation":[{"name":"Southwest Jiaotong Univ.","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yifan","family":"Wei","sequence":"additional","affiliation":[{"name":"Southwest Jiaotong Univ.","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9874-1100","authenticated-orcid":true,"given":"You","family":"Zhan","sequence":"additional","affiliation":[{"name":"Southwest Jiaotong Univ.","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"30","reference":[{"key":"e_1_3_4_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.advengsoft.2006.06.002"},{"issue":"1","key":"e_1_3_4_3_1","first-page":"751","article-title":"A pooling method developed for use in convolutional neural networks","volume":"141","author":"Akg\u00fcl \u0130.","year":"2024","unstructured":"Akg\u00fcl, \u0130. 2024. \u201cA pooling method developed for use in convolutional neural networks.\u201d CMES-Comp. Model. Eng. Sci. 141 (1): 751\u2013770. https:\/\/doi.org\/10.32604\/cmes.2024.052549.","journal-title":"CMES-Comp. Model. Eng. Sci."},{"key":"e_1_3_4_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2644615"},{"key":"e_1_3_4_5_1","doi-asserted-by":"publisher","DOI":"10.3390\/app13148181"},{"key":"e_1_3_4_6_1","doi-asserted-by":"crossref","unstructured":"Cai Y. X. Fu Y. Shang and J. Shi. 2018. \u201cMethods for long-distance crack location and detection of concrete bridge structures.\u201d In Proc. 2018 IEEE 3rd Int. Conf. on Image Vision and Computing (ICIVC). New York: IEEE.","DOI":"10.1109\/ICIVC.2018.8492764"},{"key":"e_1_3_4_7_1","doi-asserted-by":"crossref","unstructured":"Cao H. Y. Wang J. Chen D. Jiang X. Zhang Q. Tian and M. Wang. 2022. \u201cSwin-Unet: Unet-like pure transformer for medical image segmentation.\u201d In Proc. European Conf. on Computer Vision. Cham Switzerland: Springer.","DOI":"10.1007\/978-3-031-25066-8_9"},{"key":"e_1_3_4_8_1","doi-asserted-by":"publisher","DOI":"10.1111\/mice.12263"},{"key":"e_1_3_4_9_1","doi-asserted-by":"publisher","DOI":"10.3390\/app13148217"},{"key":"e_1_3_4_10_1","volume-title":"Computer Vision\u2014ECCV 2018, Lecture Notes in Computer Science","author":"Chen L.-C.","year":"2018","unstructured":"Chen, L.-C., Y. Zhu, G. Papandreou, F. Schroff, and H. Adam. 2018. \u201cEncoder\u2013decoder with atrous separable convolution for semantic image segmentation.\u201d In Vol. 11211 of Computer Vision\u2014ECCV 2018, Lecture Notes in Computer Science, edited by V. Ferrari, M. Hebert, C. Sminchisescu, and Y. Weiss. Cham, Switzerland: Springer. https:\/\/doi.org\/10.1007\/978-3-030-01234-2_49."},{"key":"e_1_3_4_11_1","doi-asserted-by":"publisher","DOI":"10.3390\/buildings14051442"},{"key":"e_1_3_4_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2023.105194"},{"key":"e_1_3_4_13_1","doi-asserted-by":"publisher","DOI":"10.1561\/0600000095"},{"key":"e_1_3_4_14_1","doi-asserted-by":"crossref","unstructured":"Durall R. M. Keuper and J. Keuper. 2020. \u201cWatch your up-convolution: CNN based generative deep neural networks are failing to reproduce spectral distributions.\u201d In Proc. IEEE\/CVF Conf. on Computer Vision and Pattern Recognition. New York: IEEE.","DOI":"10.1109\/CVPR42600.2020.00791"},{"key":"e_1_3_4_15_1","doi-asserted-by":"publisher","DOI":"10.3390\/s25144436"},{"key":"e_1_3_4_16_1","doi-asserted-by":"publisher","DOI":"10.3390\/jmse9060671"},{"key":"e_1_3_4_17_1","doi-asserted-by":"crossref","unstructured":"Fu R. Q. Hu X. Dong Y. Gao B. Li and P. Zhong. 2024. \u201cLighten CARAFE: Dynamic lightweight upsampling with guided reassemble kernels.\u201d In Proc. Int. Conf. on Pattern Recognition. Cham Switzerland: Springer.","DOI":"10.2139\/ssrn.4671723"},{"key":"e_1_3_4_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.conbuildmat.2024.136573"},{"key":"e_1_3_4_19_1","doi-asserted-by":"publisher","DOI":"10.3390\/app122110754"},{"key":"e_1_3_4_20_1","unstructured":"Hassanein A. S. S. Mohammad M. Sameer and M. E. Ragab. 2015. \u201cA survey on Hough transform theory techniques and applications.\u201d Preprint submitted February 7 2015. https:\/\/arxiv.org\/abs\/1502.02160."},{"key":"e_1_3_4_21_1","doi-asserted-by":"crossref","unstructured":"He K. X. Zhang S. Ren and J. Sun. 2016. \u201cDeep residual learning for image recognition.\u201d In Proc. IEEE Conf. on Computer Vision and Pattern Recognition. New York: IEEE.","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_4_22_1","doi-asserted-by":"publisher","DOI":"10.3390\/app132111657"},{"key":"e_1_3_4_23_1","doi-asserted-by":"publisher","DOI":"10.1007\/s13349-023-00750-0"},{"key":"e_1_3_4_24_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2024.105601"},{"key":"e_1_3_4_25_1","doi-asserted-by":"publisher","DOI":"10.1002\/stc.2551"},{"key":"e_1_3_4_26_1","doi-asserted-by":"publisher","DOI":"10.3390\/buildings14113425"},{"key":"e_1_3_4_27_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2022.104229"},{"key":"e_1_3_4_28_1","doi-asserted-by":"publisher","DOI":"10.3390\/app14188132"},{"key":"e_1_3_4_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2020.3033170"},{"key":"e_1_3_4_30_1","doi-asserted-by":"crossref","unstructured":"Kamada S. T. Ichimura and T. Iwasaki. 2020. \u201cAn adaptive structural learning of deep belief network for image-based crack detection in concrete structures using SDNET2018.\u201d In 2020 Int. Conf. on Image Processing and Robotics (ICIP) Negombo Sri Lanka 1\u20136. Piscataway NJ: IEEE. https:\/\/doi.org\/10.1109\/ICIP48927.2020.9367339.","DOI":"10.1109\/ICIP48927.2020.9367339"},{"key":"e_1_3_4_31_1","doi-asserted-by":"publisher","DOI":"10.3390\/s17092052"},{"key":"e_1_3_4_32_1","doi-asserted-by":"publisher","DOI":"10.1049\/ipr2.12512"},{"key":"e_1_3_4_33_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.istruc.2024.106321"},{"key":"e_1_3_4_34_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2021.109171"},{"key":"e_1_3_4_35_1","doi-asserted-by":"publisher","DOI":"10.1587\/transinf.2018EDL8150"},{"key":"e_1_3_4_36_1","unstructured":"Lin F. J. Yang J. Shu and R. J. Scherer. 2021. \u201cCrack semantic segmentation using the U-Net with full attention strategy.\u201d Preprint submitted April 29 2021. https:\/\/arxiv.org\/abs\/2104.14586."},{"key":"e_1_3_4_37_1","doi-asserted-by":"crossref","unstructured":"Lindeberg T. 2012. \u201cScale invariant feature transform.\u201d Scholarpedia 7: 10491. https:\/\/doi.org\/10.4249\/scholarpedia.10491.","DOI":"10.4249\/scholarpedia.10491"},{"key":"e_1_3_4_38_1","unstructured":"Liu J. Y. Wei and B. Chen. 2022. \u201cA hierarchical semantic segmentation framework for computer vision-based bridge damage detection.\u201d Preprint submitted July 18 2022. https:\/\/arxiv.org\/abs\/2207.08878."},{"key":"e_1_3_4_39_1","doi-asserted-by":"crossref","unstructured":"Liu W. H. Lu H. Fu and Z. Cao. 2023a. \u201cLearning to upsample by learning to sample.\u201d In Proc. IEEE\/CVF Int. Conf. on Computer Vision. New York: IEEE.","DOI":"10.1109\/ICCV51070.2023.00554"},{"key":"e_1_3_4_40_1","doi-asserted-by":"publisher","DOI":"10.3390\/app13179878"},{"key":"e_1_3_4_41_1","doi-asserted-by":"crossref","unstructured":"Lou A. S. Guan and M. Loew. 2021. \u201cDC-UNet: Rethinking the U-Net architecture with dual channel efficient CNN for medical image segmentation.\u201d In Proc. Medical Imaging 2021: Image Processing Vol. 11596 758\u2013768. Bellingham WA: SPIE.","DOI":"10.1117\/12.2582338"},{"key":"e_1_3_4_42_1","doi-asserted-by":"publisher","DOI":"10.3390\/app14125004"},{"key":"e_1_3_4_43_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2020.103415"},{"key":"e_1_3_4_44_1","doi-asserted-by":"crossref","unstructured":"Milletari F. N. Navab and S.-A. Ahmadi. 2016. \u201cV-Net: Fully convolutional neural networks for volumetric medical image segmentation.\u201d In 2016 Fourth Int. Conf. on 3D Vision (3DV) 565\u2013571. Piscataway NJ: IEEE. https:\/\/doi.org\/10.1109\/3DV.2016.79.","DOI":"10.1109\/3DV.2016.79"},{"key":"e_1_3_4_45_1","doi-asserted-by":"publisher","DOI":"10.23915\/distill.00003"},{"key":"e_1_3_4_46_1","doi-asserted-by":"publisher","DOI":"10.3390\/s22103662"},{"key":"e_1_3_4_47_1","doi-asserted-by":"publisher","DOI":"10.3390\/s21092902"},{"key":"e_1_3_4_48_1","doi-asserted-by":"crossref","unstructured":"Ronneberger O. P. Fischer and T. Brox. 2015. \u201cU-net: Convolutional networks for biomedical image segmentation.\u201d In Proc. Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015: 18th Int. Conf. Cham Switzerland: Springer.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"e_1_3_4_49_1","unstructured":"Ruiqiang X. 2022. \u201cYOLOv5s-GTB: Light-weighted and improved YOLOv5s for bridge crack detection.\u201d Preprint submitted June 3 2022. https:\/\/arxiv.org\/abs\/2206.01498."},{"key":"e_1_3_4_50_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.conbuildmat.2024.136136"},{"key":"e_1_3_4_51_1","doi-asserted-by":"publisher","DOI":"10.3390\/buildings14123928"},{"key":"e_1_3_4_52_1","doi-asserted-by":"publisher","DOI":"10.3390\/buildings12101561"},{"key":"e_1_3_4_53_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.106669"},{"key":"e_1_3_4_54_1","unstructured":"Vaswani A. 2017. \u201cAttention is all you need.\u201d In Proc. Advances in Neural Information Processing Systems. Red Hook NY: Curran Associates."},{"key":"e_1_3_4_55_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.matpr.2022.11.356"},{"key":"e_1_3_4_56_1","doi-asserted-by":"crossref","unstructured":"Wang L. and Y. Sun. 2021. \u201cImproved Canny edge detection algorithm.\u201d In 2021 2nd Int. Conf. on Computer Science and Management Technology (ICCSMT) Shanghai China 414\u2013417. Piscataway NJ: IEEE. https:\/\/doi.org\/10.1109\/ICCSMT54525.2021.00081.","DOI":"10.1109\/ICCSMT54525.2021.00081"},{"key":"e_1_3_4_57_1","doi-asserted-by":"crossref","unstructured":"Wang P. P. Chen Y. Yuan D. Liu Z. Huang X. Hou and G. Cottrell. 2018. \u201cUnderstanding convolution for semantic segmentation.\u201d In Proc. 2018 IEEE Winter Conf. on Applications of Computer Vision (WACV). New York: IEEE.","DOI":"10.1109\/WACV.2018.00163"},{"key":"e_1_3_4_58_1","doi-asserted-by":"crossref","unstructured":"Wang Q. B. Wu P. Zhu P. Li W. Zuo and Q. Hu. 2020. \u201cECA-Net: Efficient channel attention for deep convolutional neural networks.\u201d In Proc. IEEE\/CVF Conf. on Computer Vision and Pattern Recognition. New York: IEEE.","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"e_1_3_4_59_1","doi-asserted-by":"crossref","unstructured":"Woo S. J. Park J.-Y. Lee and I.-S. Kweon. 2018. \u201cCBAM: Convolutional block attention module.\u201d In Vol. 11211 of Computer Vision\u2014ECCV 2018 Lecture Notes in Computer Science edited by V. Ferrari M. Hebert C. Sminchisescu and Y. Weiss 3\u201319. Cham Switzerland: Springer. https:\/\/doi.org\/10.1007\/978-3-030-01234-2_49.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"e_1_3_4_60_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.iintel.2025.100144"},{"key":"e_1_3_4_61_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2023.105226"},{"key":"e_1_3_4_62_1","unstructured":"Xie E. W. Wang Z. Yu A. Anandkumar J. M. Alvarez and P. Luo. 2021. \u201cSegFormer: Simple and efficient design for semantic segmentation with transformers.\u201d In Vol. 34 of Proc. Advances in Neural Information Processing Systems 12077\u201312090. Red Hook NY: Curran Associates."},{"key":"e_1_3_4_63_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-69722-8"},{"key":"e_1_3_4_64_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2025.110411"},{"key":"e_1_3_4_65_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2019.2910595"},{"key":"e_1_3_4_66_1","doi-asserted-by":"crossref","unstructured":"Yang T. Q. Zhao and X. Wang. 2019b. \u201cAutomatic detection and dimensional measurement of concrete bridge crack based on machine vision.\u201d In Proc. Seventh Int. Conf. on Optical and Photonic Engineering (icOPEN 2019) 1120528. Bellingham WA: SPIE. https:\/\/doi.org\/10.1117\/12.2548269.","DOI":"10.1117\/12.2548269"},{"key":"e_1_3_4_67_1","first-page":"100436","article-title":"An improved U-Net model for concrete crack detection","volume":"10","author":"Yu C.","year":"2022","unstructured":"Yu, C., J. Du, M. Li, Y. Li, and W. Li. 2022. \u201cAn improved U-Net model for concrete crack detection.\u201d Mach. Learn. Appl. 10 (Dec): 100436. https:\/\/doi.org\/10.1016\/j.mlwa.2022.100436.","journal-title":"Mach. Learn. Appl."},{"key":"e_1_3_4_68_1","doi-asserted-by":"crossref","unstructured":"Yu X. X. Wang X. Da and J. Zhao. 2020. \u201cCrack detection algorithm of complex bridge based on image process.\u201d In Proc. CICTP 2020 1341\u20131353. Reston VA: ASCE.","DOI":"10.1061\/9780784482933.115"},{"key":"e_1_3_4_69_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2020.103514"},{"key":"e_1_3_4_70_1","doi-asserted-by":"publisher","DOI":"10.3390\/app12178643"},{"key":"e_1_3_4_71_1","doi-asserted-by":"publisher","DOI":"10.1111\/mice.12909"},{"key":"e_1_3_4_72_1","doi-asserted-by":"publisher","DOI":"10.1111\/mice.12500"},{"key":"e_1_3_4_73_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jobe.2023.107314"},{"key":"e_1_3_4_74_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2024.3360263"},{"key":"e_1_3_4_75_1","doi-asserted-by":"publisher","DOI":"10.1080\/10589759.2024.2338921"},{"key":"e_1_3_4_76_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2022.105225"},{"key":"e_1_3_4_77_1","doi-asserted-by":"publisher","DOI":"10.1007\/s40747-022-00876-6"},{"key":"e_1_3_4_78_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ifacol.2020.12.1994"},{"key":"e_1_3_4_79_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-025-91352-x"},{"key":"e_1_3_4_80_1","doi-asserted-by":"crossref","unstructured":"Zhao H. J. Shi X. Qi X. Wang and J. Jia. 2017. \u201cPyramid scene parsing network.\u201d In Proc. IEEE Conf. Computer Vision and Pattern Recognition. New York: IEEE.","DOI":"10.1109\/CVPR.2017.660"},{"key":"e_1_3_4_81_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3133712"},{"key":"e_1_3_4_82_1","doi-asserted-by":"publisher","DOI":"10.3390\/buildings15071117"}],"container-title":["Journal of Computing in Civil Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/ascelibrary.org\/doi\/pdf\/10.1061\/JCCEE5.CPENG-7247","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T08:58:45Z","timestamp":1769158725000},"score":1,"resource":{"primary":{"URL":"https:\/\/ascelibrary.org\/doi\/10.1061\/JCCEE5.CPENG-7247"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5]]},"references-count":81,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2026,5]]}},"alternative-id":["10.1061\/JCCEE5.CPENG-7247"],"URL":"https:\/\/doi.org\/10.1061\/jccee5.cpeng-7247","relation":{},"ISSN":["0887-3801","1943-5487"],"issn-type":[{"value":"0887-3801","type":"print"},{"value":"1943-5487","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5]]},"assertion":[{"value":"2025-06-06","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-09-09","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-01-23","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"04026007"}}