{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T16:15:25Z","timestamp":1783181725273,"version":"3.54.6"},"reference-count":64,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100002920","name":"Research Grants Council, University Grants Committee","doi-asserted-by":"publisher","award":["16205021"],"award-info":[{"award-number":["16205021"]}],"id":[{"id":"10.13039\/501100002920","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010428","name":"Innovation and Technology Fund","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100010428","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Expert Systems with Applications"],"published-print":{"date-parts":[[2026,11]]},"DOI":"10.1016\/j.eswa.2026.133035","type":"journal-article","created":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T15:58:17Z","timestamp":1779897497000},"page":"133035","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["CrackMamba: An attention-guided Mamba architecture for efficient and edge-ready crack segmentation using global and local information"],"prefix":"10.1016","volume":"329","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5616-9994","authenticated-orcid":false,"given":"Zhili","family":"He","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wai Yi","family":"Chau","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mei Ling","family":"Leung","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yingyue","family":"Han","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wessam","family":"Hamid","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7829-2750","authenticated-orcid":false,"given":"Yu-Hsing","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.eswa.2026.133035_b0005","doi-asserted-by":"crossref","DOI":"10.1016\/j.autcon.2023.105153","article-title":"Automated vision-based structural health inspection and assessment for post-construction civil infrastructure","volume":"156","author":"Agyemang","year":"2023","journal-title":"Automation in Construction"},{"key":"10.1016\/j.eswa.2026.133035_b0010","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.128144","article-title":"HACNet V2: Rethinking the full-resolution architecture for pixel-level crack detection","volume":"286","author":"Chen","year":"2025","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2026.133035_b0015","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2024.103280","article-title":"TransUNet: Rethinking the U-Net architecture design for medical image segmentation through the lens of transformers","volume":"97","author":"Chen","year":"2024","journal-title":"Medical Image Analysis"},{"key":"10.1016\/j.eswa.2026.133035_b0020","series-title":"Proceedings of the European Conference on Computer Vision (ECCV)","first-page":"833","article-title":"Encoder-decoder with atrous separable convolution for semantic image segmentation","author":"Chen","year":"2018"},{"key":"10.1016\/j.eswa.2026.133035_b0025","doi-asserted-by":"crossref","DOI":"10.1016\/j.autcon.2023.105022","article-title":"Online monitoring of crack dynamic development using attention-based deep networks","volume":"154","author":"Chen","year":"2023","journal-title":"Automation in Construction"},{"key":"10.1016\/j.eswa.2026.133035_b0030","doi-asserted-by":"crossref","DOI":"10.1016\/j.engstruct.2024.117759","article-title":"Time-variant seismic resilience of reinforced concrete buildings subjected to spatiotemporal random deterioration","volume":"305","author":"Chen","year":"2024","journal-title":"Engineering Structures"},{"key":"10.1016\/j.eswa.2026.133035_b0035","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2020.101105","article-title":"Anomaly detection of defects on concrete structures with the convolutional Autoencoder","volume":"45","author":"Chow","year":"2020","journal-title":"Advanced Engineering Informatics"},{"key":"10.1016\/j.eswa.2026.133035_b0040","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2024.102508","article-title":"Cascade operation-enhanced high-resolution representation learning for meticulous segmentation of bridge cracks","volume":"61","author":"Chu","year":"2024","journal-title":"Advanced Engineering Informatics"},{"issue":"4","key":"10.1016\/j.eswa.2026.133035_b0045","doi-asserted-by":"crossref","first-page":"575","DOI":"10.1111\/mice.13111","article-title":"Fine\u2010grained crack segmentation for high\u2010resolution images via a multiscale cascaded network","volume":"39","author":"Chu","year":"2023","journal-title":"Computer-Aided Civil and Infrastructure Engineering"},{"issue":"14","key":"10.1016\/j.eswa.2026.133035_b0050","doi-asserted-by":"crossref","first-page":"1914","DOI":"10.1111\/mice.12881","article-title":"Tiny\u2010Crack\u2010Net: A multiscale feature fusion network with attention mechanisms for segmentation of tiny cracks","volume":"37","author":"Chu","year":"2022","journal-title":"Computer-Aided Civil and Infrastructure Engineering"},{"key":"10.1016\/j.eswa.2026.133035_b0055","article-title":"LoG-VMamba: Local-global vision mamba for medical image segmentation","author":"Dang","year":"2024","journal-title":"arXiv"},{"key":"10.1016\/j.eswa.2026.133035_b0060","article-title":"FlashAttention: Fast and memory-efficient exact attention with IO-Awareness","author":"Dao","year":"2022","journal-title":"arXiv"},{"key":"10.1016\/j.eswa.2026.133035_b0065","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"11953","article-title":"Scaling up your kernels to 31x31: Revisiting large kernel design in cnns","author":"Ding","year":"2022"},{"key":"10.1016\/j.eswa.2026.133035_b0070","article-title":"An image is worth 16x16 words: Transformers for image recognition at scale","author":"Dosovitskiy","year":"2021","journal-title":"International Conference on Learning Representations"},{"issue":"11","key":"10.1016\/j.eswa.2026.133035_b0075","doi-asserted-by":"crossref","first-page":"1659","DOI":"10.1111\/mice.13166","article-title":"Vision-based fatigue crack automatic perception and geometric updating of finite element model for welded joint in steel structures","volume":"39","author":"Gao","year":"2024","journal-title":"Computer-Aided Civil and Infrastructure Engineering"},{"key":"10.1016\/j.eswa.2026.133035_b0080","article-title":"Mamba: Linear-time sequence modeling with selective state spaces","author":"Gu","year":"2024","journal-title":"arXiv"},{"key":"10.1016\/j.eswa.2026.133035_b0085","article-title":"Efficiently modeling long sequences with structured state spaces","author":"Gu","year":"2022","journal-title":"arXiv"},{"issue":"10","key":"10.1016\/j.eswa.2026.133035_b0090","doi-asserted-by":"crossref","first-page":"2281","DOI":"10.1109\/TMI.2019.2903562","article-title":"CE-Net: Context encoder network for 2D medical image segmentation","volume":"38","author":"Gu","year":"2019","journal-title":"IEEE Transactions on Medical Imaging"},{"issue":"Part D","key":"10.1016\/j.eswa.2026.133035_b0095","article-title":"ABE-Mamba: Few-shot medical image segmentation via adversarial bidirectional enhanced Mamba","volume":"298","author":"Guo","year":"2026","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2026.133035_b0100","doi-asserted-by":"crossref","DOI":"10.1016\/j.autcon.2023.105186","article-title":"Surface defect detection of civil structures using images: Review from data perspective","volume":"158","author":"Guo","year":"2024","journal-title":"Automation in Construction"},{"key":"10.1016\/j.eswa.2026.133035_b0105","doi-asserted-by":"crossref","DOI":"10.1016\/j.autcon.2024.105770","article-title":"Enhancing pixel-level crack segmentation with visual mamba and convolutional networks","volume":"168","author":"Han","year":"2024","journal-title":"Automation in Construction"},{"key":"10.1016\/j.eswa.2026.133035_b0110","doi-asserted-by":"crossref","DOI":"10.1016\/j.autcon.2024.105354","article-title":"Crack segmentation on steel structures using boundary guidance model","volume":"162","author":"He","year":"2024","journal-title":"Automation in Construction"},{"key":"10.1016\/j.eswa.2026.133035_b0115","doi-asserted-by":"crossref","DOI":"10.1016\/j.autcon.2021.104017","article-title":"Automatic damage detection using anchor-free method and unmanned surface vessel","volume":"133","author":"He","year":"2022","journal-title":"Automation in Construction"},{"key":"10.1016\/j.eswa.2026.133035_b0120","doi-asserted-by":"crossref","DOI":"10.1016\/j.mtcomm.2021.102719","article-title":"A physics-informed deep learning method for solving direct and inverse heat conduction problems of materials","volume":"28","author":"He","year":"2021","journal-title":"Materials Today Communications"},{"key":"10.1016\/j.eswa.2026.133035_b0125","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2024.102654","article-title":"Generative AIBIM: An automatic and intelligent structural design pipeline integrating BIM and generative AI","volume":"114","author":"He","year":"2025","journal-title":"Information Fusion"},{"key":"10.1016\/j.eswa.2026.133035_b0130","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2025.113123","article-title":"An automatic method for measuring inter-storey structural displacement based on vision data","volume":"237","author":"Hou","year":"2025","journal-title":"Mechanical Systems and Signal Processing"},{"issue":"8","key":"10.1016\/j.eswa.2026.133035_b0135","doi-asserted-by":"crossref","first-page":"2011","DOI":"10.1109\/TPAMI.2019.2913372","article-title":"Squeeze-and-excitation networks","volume":"42","author":"Hu","year":"2020","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"10.1016\/j.eswa.2026.133035_b0140","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2023.102279","article-title":"Cross teacher pseudo supervision: Enhancing semi-supervised crack segmentation with consistency learning","volume":"59","author":"Jian","year":"2024","journal-title":"Advanced Engineering Informatics"},{"key":"10.1016\/j.eswa.2026.133035_b0145","series-title":"Proceedings of the 37th International Conference on Machine Learning","first-page":"5156","article-title":"Transformers are RNNs: Fast autoregressive transformers with linear attention","author":"Katharopoulos","year":"2020"},{"key":"10.1016\/j.eswa.2026.133035_b0150","doi-asserted-by":"crossref","DOI":"10.1016\/j.autcon.2024.105482","article-title":"CNN-based network with multi-scale context feature and attention mechanism for automatic pavement crack segmentation","volume":"164","author":"Liang","year":"2024","journal-title":"Automation in Construction"},{"key":"10.1016\/j.eswa.2026.133035_b0155","article-title":"VMamba: Visual state space model","author":"Liu","year":"2024","journal-title":"arXiv"},{"key":"10.1016\/j.eswa.2026.133035_b0160","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.neucom.2019.01.036","article-title":"DeepCrack: A deep hierarchical feature learning architecture for crack segmentation","volume":"338","author":"Liu","year":"2019","journal-title":"Neurocomputing"},{"key":"10.1016\/j.eswa.2026.133035_b0165","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV)","first-page":"9992","article-title":"Swin transformer: Hierarchical vision transformer using shifted windows","author":"Liu","year":"2021"},{"key":"10.1016\/j.eswa.2026.133035_b0170","doi-asserted-by":"crossref","DOI":"10.1016\/j.autcon.2023.104744","article-title":"Automated detection of dangerous work zone for crawler crane guided by UAV images via Swin Transformer","volume":"147","author":"Lu","year":"2023","journal-title":"Automation in Construction"},{"key":"10.1016\/j.eswa.2026.133035_b0175","first-page":"4905","article-title":"Understanding the effective receptive field in deep convolutional neural networks","volume":"29","author":"Luo","year":"2016","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.eswa.2026.133035_b0180","article-title":"RS3Mamba: Visual state space model for remote sensing images semantic segmentation","author":"Ma","year":"2024","journal-title":"arXiv"},{"key":"10.1016\/j.eswa.2026.133035_b0185","article-title":"MobileViT: Lightweight, general-purpose, and mobile-friendly vision transformer","author":"Mehta","year":"2022","journal-title":"International Conference on Learning Representations"},{"key":"10.1016\/j.eswa.2026.133035_b0190","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2022.101575","article-title":"A Generative adversarial learning strategy for enhanced lightweight crack delineation networks","volume":"52","author":"Ni","year":"2022","journal-title":"Advanced Engineering Informatics"},{"key":"10.1016\/j.eswa.2026.133035_b0195","article-title":"Attention U-Net: Learning where to look for the pancreas","author":"Oktay","year":"2018","journal-title":"arXiv"},{"issue":"26","key":"10.1016\/j.eswa.2026.133035_b0200","doi-asserted-by":"crossref","first-page":"4521","DOI":"10.1111\/mice.70065","article-title":"Automated detection system of metro tunnel lining crack using dynamic snake convolution","volume":"40","author":"Pan","year":"2025","journal-title":"Computer-Aided Civil and Infrastructure Engineering"},{"key":"10.1016\/j.eswa.2026.133035_b0205","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.128683","article-title":"Enhanced concrete crack segmentation with MSMC-U-Net: Integrating multiscale features and contextual analysis for infrastructure safety","volume":"293","author":"Pervaiz","year":"2025","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2026.133035_b0210","series-title":"Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)","first-page":"234","article-title":"U-Net: Convolutional networks for biomedical image segmentation","author":"Ronneberger","year":"2015"},{"key":"10.1016\/j.eswa.2026.133035_b0215","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.125891","article-title":"DCUFormer: Enhancing pavement crack segmentation in complex environments with dual-cross\/upsampling attention","volume":"264","author":"Shan","year":"2025","journal-title":"Expert Systems with Applications"},{"issue":"12","key":"10.1016\/j.eswa.2026.133035_b0220","doi-asserted-by":"crossref","first-page":"3434","DOI":"10.1109\/TITS.2016.2552248","article-title":"Automatic road crack detection using random structured forests","volume":"17","author":"Shi","year":"2016","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"issue":"Part A","key":"10.1016\/j.eswa.2026.133035_b0225","article-title":"MOD-YOLO: Rethinking the YOLO architecture at the level of feature information and applying it to crack detection","volume":"237","author":"Su","year":"2024","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2026.133035_b0230","first-page":"6000","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.eswa.2026.133035_b0235","article-title":"The lightweight CS-YOLO model applied for concrete crack segmentation","volume":"277","author":"Wang","year":"2025","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2026.133035_b0240","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.130064","article-title":"DSA mamba: A model for advanced medical image classification","volume":"299","author":"Wang","year":"2026","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2026.133035_b0245","article-title":"Mamba-UNet: UNet-like pure visual mamba for medical image segmentation","author":"Wang","year":"2024","journal-title":"arXiv"},{"key":"10.1016\/j.eswa.2026.133035_b0250","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2025.110603","article-title":"Comprehensive assessment of failure mode and shear capacity of reinforced concrete circular columns based on data-driven machine learning methods","volume":"150","author":"Wen","year":"2025","journal-title":"Engineering Applications of Artificial Intelligence"},{"key":"10.1016\/j.eswa.2026.133035_b0255","series-title":"Proceedings of the European Conference on Computer Vision (ECCV)","first-page":"3","article-title":"CBAM: Convolutional block attention module","author":"Woo","year":"2018"},{"key":"10.1016\/j.eswa.2026.133035_b0260","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV)","first-page":"7263","article-title":"Stacked cross refinement network for edge-aware salient object detection","author":"Wu","year":"2019"},{"key":"10.1016\/j.eswa.2026.133035_b0265","article-title":"PlainMamba: Improving non-hierarchical mamba in visual recognition","author":"Yang","year":"2024","journal-title":"arXiv"},{"issue":"4","key":"10.1016\/j.eswa.2026.133035_b0270","doi-asserted-by":"crossref","first-page":"1525","DOI":"10.1109\/TITS.2019.2910595","article-title":"Feature pyramid and hierarchical boosting network for pavement crack detection","volume":"21","author":"Yang","year":"2020","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"key":"10.1016\/j.eswa.2026.133035_b0275","doi-asserted-by":"crossref","DOI":"10.1016\/j.autcon.2023.105254","article-title":"UAV-deployed deep learning network for real-time multi-class damage detection using model quantization techniques","volume":"159","author":"Yang","year":"2024","journal-title":"Automation in Construction"},{"key":"10.1016\/j.eswa.2026.133035_b0280","article-title":"Vivim: A video vision Mamba for medical video object segmentation","author":"Yang","year":"2024","journal-title":"arXiv"},{"key":"10.1016\/j.eswa.2026.133035_b0285","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"11525","article-title":"Directional connectivity-based segmentation of medical images","author":"Yang","year":"2023"},{"issue":"1","key":"10.1016\/j.eswa.2026.133035_b0290","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1186\/s43251-022-00078-7","article-title":"The application of deep learning in bridge health monitoring: A literature review","volume":"3","author":"Zhang","year":"2022","journal-title":"Advances in Bridge Engineering"},{"key":"10.1016\/j.eswa.2026.133035_b0295","doi-asserted-by":"crossref","DOI":"10.1016\/j.engstruct.2023.116023","article-title":"Fully decouple convolutional network for damage detection of rebars in RC beams","volume":"285","author":"Zhang","year":"2023","journal-title":"Engineering Structures"},{"issue":"12","key":"10.1016\/j.eswa.2026.133035_b0300","doi-asserted-by":"crossref","first-page":"1617","DOI":"10.1111\/mice.12712","article-title":"Vortex-induced vibration measurement of a long-span suspension bridge through noncontact sensing strategies","volume":"37","author":"Zhang","year":"2022","journal-title":"Computer-Aided Civil and Infrastructure Engineering"},{"issue":"1","key":"10.1016\/j.eswa.2026.133035_b0305","doi-asserted-by":"crossref","first-page":"3348","DOI":"10.1080\/17538947.2023.2247390","article-title":"Automatic identification of building structure types using unmanned aerial vehicle oblique images and deep learning considering facade prior knowledge","volume":"16","author":"Zhang","year":"2023","journal-title":"International Journal of Digital Earth"},{"issue":"6","key":"10.1016\/j.eswa.2026.133035_b0310","doi-asserted-by":"crossref","first-page":"1856","DOI":"10.1109\/TMI.2019.2959609","article-title":"UNet++: Redesigning skip connections to exploit multiscale features in image segmentation","volume":"39","author":"Zhou","year":"2020","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"10.1016\/j.eswa.2026.133035_b0315","article-title":"Vision Mamba: Efficient visual representation learning with bidirectional state space model","author":"Zhu","year":"2024","journal-title":"arXiv"},{"key":"10.1016\/j.eswa.2026.133035_b0320","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/j.patrec.2011.11.004","article-title":"CrackTree: Automatic crack detection from pavement images","volume":"33","author":"Zou","year":"2012","journal-title":"Pattern Recognition Letters"}],"container-title":["Expert Systems with Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417426019469?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417426019469?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T15:18:43Z","timestamp":1783178323000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0957417426019469"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,11]]},"references-count":64,"alternative-id":["S0957417426019469"],"URL":"https:\/\/doi.org\/10.1016\/j.eswa.2026.133035","relation":{},"ISSN":["0957-4174"],"issn-type":[{"value":"0957-4174","type":"print"}],"subject":[],"published":{"date-parts":[[2026,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"CrackMamba: An attention-guided Mamba architecture for efficient and edge-ready crack segmentation using global and local information","name":"articletitle","label":"Article Title"},{"value":"Expert Systems with Applications","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.eswa.2026.133035","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"133035"}}