{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:05:48Z","timestamp":1760144748842,"version":"build-2065373602"},"reference-count":23,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2024,5,20]],"date-time":"2024-05-20T00:00:00Z","timestamp":1716163200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001871","name":"FCT (Portuguese Foundation for Science and Technology)","doi-asserted-by":"publisher","award":["UIDB\/04082\/2020 (CMADE)"],"award-info":[{"award-number":["UIDB\/04082\/2020 (CMADE)"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Applied Sciences"],"abstract":"<jats:p>Pathologies in concrete structures can be visually evidenced on the concrete surface, such as by fissures or cracks, fragmentation of part of the concrete, concrete efflorescence, corrosion stains on the concrete surface, or exposed steel bars, the latter two occurring in reinforced concrete. Therefore, these pathologies can be analyzed via the images of concrete structures. This article proposes a methodology for visually inspecting concrete structures using deep neural networks. This method makes it possible to speed up the detection task and increase its effectiveness by saving time in preparing the identifications to be analyzed and eliminating or reducing errors, such as those resulting from human errors caused by the execution of tedious, repetitive analysis tasks. The methodology was tested to analyze its accuracy. The neural network architecture used for detection was YOLO, versions 4 and 8, which was tested to analyze the gain with migration to a more recent version. The dataset for classification was Ozgnel, which was trained with YOLO version 8, and the detection dataset was CODEBRIM. The use of a dedicated classification dataset allows for a better-trained network for this function and results in the elimination of false positives in the detection stage. The classification achieved 99.65% accuracy.<\/jats:p>","DOI":"10.3390\/app14104332","type":"journal-article","created":{"date-parts":[[2024,5,20]],"date-time":"2024-05-20T11:06:41Z","timestamp":1716203201000},"page":"4332","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A One-Step Methodology for Identifying Concrete Pathologies Using Neural Networks\u2014Using YOLO v8 and Dataset Review"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-0889-9051","authenticated-orcid":false,"given":"Joel de Concei\u00e7\u00e3o Nogueira","family":"Diniz","sequence":"first","affiliation":[{"name":"UFMA\/Computer Science Department, Universidade Federal do Maranh\u00e3o, Campus do Bacanga, S\u00e3o Lu\u00eds 65085-580, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4921-0626","authenticated-orcid":false,"given":"Anselmo Cardoso","family":"de Paiva","sequence":"additional","affiliation":[{"name":"UFMA\/Computer Science Department, Universidade Federal do Maranh\u00e3o, Campus do Bacanga, S\u00e3o Lu\u00eds 65085-580, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3731-6431","authenticated-orcid":false,"given":"Geraldo Braz","family":"Junior","sequence":"additional","affiliation":[{"name":"UFMA\/Computer Science Department, Universidade Federal do Maranh\u00e3o, Campus do Bacanga, S\u00e3o Lu\u00eds 65085-580, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7013-9700","authenticated-orcid":false,"given":"Jo\u00e3o Dallyson Sousa","family":"de Almeida","sequence":"additional","affiliation":[{"name":"UFMA\/Computer Science Department, Universidade Federal do Maranh\u00e3o, Campus do Bacanga, S\u00e3o Lu\u00eds 65085-580, Brazil"}]},{"given":"Arist\u00f3fanes Corr\u00eaa","family":"Silva","sequence":"additional","affiliation":[{"name":"UFMA\/Computer Science Department, Universidade Federal do Maranh\u00e3o, Campus do Bacanga, S\u00e3o Lu\u00eds 65085-580, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3458-7693","authenticated-orcid":false,"given":"Ant\u00f3nio Manuel Trigueiros da Silva","family":"Cunha","sequence":"additional","affiliation":[{"name":"UTAD\/Engineering Department, Universidade de Tr\u00e1s-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal"},{"name":"ALGORITMI Research Centre, University of Minho, 4800-058 Guimar\u00e3es, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2630-7900","authenticated-orcid":false,"given":"Sandra Cristina Alves Pereira da Silva","family":"Cunha","sequence":"additional","affiliation":[{"name":"UTAD\/Engineering Department, Universidade de Tr\u00e1s-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal"},{"name":"CMADE\u2014Centre of Materials and Building Technologies, UTAD, 5000-801 Vila Real, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,20]]},"reference":[{"key":"ref_1","unstructured":"James, K.W. (2016). Reinforced Concrete Mechanics and Design, Pearson Education Limited."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"492","DOI":"10.1016\/j.conbuildmat.2019.01.172","article-title":"A review on five key sensors for monitoring of concrete structures","volume":"204","author":"Taheri","year":"2019","journal-title":"Constr. Build. Mater."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Safiuddin, M., Kaish, A.B.M.A., Woon, C.-O., and Raman, S.N. (2018). Early-Age Cracking in Concrete: Causes, Consequences, Remedial Measures, and Recommendations. Appl. Sci., 8.","DOI":"10.3390\/app8101730"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Nogueira Diniz, J.d.C., de Paiva, A.C., Junior, G.B., de Almeida, J.D.S., Silva, A.C., Cunha, A.M.T.d.S., and Cunha, S.C.A.P.d.S. (2023). A Method for Detecting Pathologies in Concrete Structures Using Deep Neural Networks. Appl. Sci., 13.","DOI":"10.3390\/app13095763"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1016\/j.enganabound.2023.10.027","article-title":"Physical informed neural network for thermo-hydral analysis of fire-loaded concrete","volume":"158","author":"Gao","year":"2024","journal-title":"Eng. Anal. Bound. Elem."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1144606","DOI":"10.3389\/fbuil.2023.1144606","article-title":"Damage detection on steel-reinforced concrete produced by corrosion via YOLOv3: A detailed guide","volume":"9","author":"Naser","year":"2023","journal-title":"Front. Built Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2105","DOI":"10.28991\/CEJ-2023-09-09-01","article-title":"Convolutional Neural Network for Predicting Failure Type in Concrete Cylinders During Compression Testing","volume":"9","author":"Ojeda","year":"2023","journal-title":"Civ. Eng. J."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Beskopylny, A.N., Shcherban\u2019, E.M., Stel\u2019makh, S.A., Mailyan, L.R., Meskhi, B., Razveeva, I., Kozhakin, A., Beskopylny, N., El\u2019shaeva, D., and Artamonov, S. (2023). Method for Concrete Structure Analysis by Microscopy of Hardened Cement Paste and Crack Segmentation Using a Convolutional Neural Network. J. Compos. Sci., 7.","DOI":"10.3390\/jcs7080327"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s41024-021-00140-3","article-title":"Application of artificial neural networks for prediction of microbial concrete compressive strength","volume":"7","author":"Vijay","year":"2022","journal-title":"J. Build. Pathol. Rehabil."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"eabk0644","DOI":"10.1126\/sciadv.abk0644","article-title":"Analyses of internal structures and defects in materials using physics-informed neural networks","volume":"8","author":"Zhang","year":"2022","journal-title":"Sci. Adv."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"061001","DOI":"10.1115\/1.4062966","article-title":"Recent advances and applications of machine learning in experimental solid mechanics: A review","volume":"75","author":"Jin","year":"2023","journal-title":"Appl. Mech. Rev."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1999","DOI":"10.1007\/s00170-022-10335-8","article-title":"A new lightweight deep neural network for surface scratch detection","volume":"123","author":"Li","year":"2022","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_13","unstructured":"(2022, March 01). Concrete Defect Bridge Image Dataset. Available online: https:\/\/zenodo.org\/record\/2620293#.YgLkC9_MKMo."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016). You Only Look Once: Unified, Real-Time Object Detection. arXiv.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_15","unstructured":"Bochkovskiy, A., Wang, C.-Y., and Liao, H.-Y.M. (2020). YOLOv4: Optimal Speed and Accuracy of Object Detection. arXiv."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Mundt, M., Majumder, S., Murali, S., Panetsos, P., and Ramesh, V. (2019, January 15\u201320). Meta-learning Convolutional Neural Architectures for Multi-target Concrete Defect Classification with the COncrete DEfect BRidge IMage Dataset. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.01145"},{"key":"ref_17","unstructured":"(2022, January 15). Concrete Crack Images for Classification. Available online: https:\/\/data.mendeley.com\/datasets\/5y9wdsg2zt\/2."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"110157","DOI":"10.1016\/j.engstruct.2019.110157","article-title":"Increasing the robustness of material-specific deep-learning models for crack detection across different materials","volume":"206","author":"Alipour","year":"2020","journal-title":"Eng. Struct."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"411","DOI":"10.5194\/isprs-annals-V-2-2020-411-2020","article-title":"Deep Cascaded Neural Networks for Automatic Detection of Structural Damage and Cracks from Images","volume":"V-2-2020","author":"Bai","year":"2020","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_20","first-page":"1236","article-title":"Crack Detection on Concrete Images Using Classification Techniques in Machine Learning","volume":"7","author":"Jitendra","year":"2020","journal-title":"J. Crit. Rev."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Kim, J.J., Kim, A.-R., and Lee, S.-W. (2020). Artificial Neural Network-Based Automated Crack Detection and Analysis for the Inspection of Concrete Structures. Appl. Sci., 10.","DOI":"10.3390\/app10228105"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Pal, M., Palevicius, P., Landauskas, M., Orinaite, U., Timofejeva, I., and Ragulskis, M. (2021). An Overview of Challenges Associated with Automatic Detection of Concrete Cracks in the Presence of Shadows. Appl. Sci., 11.","DOI":"10.3390\/app112311396"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"142","DOI":"10.21595\/vp.2022.22845","article-title":"A deep learning-based approach for automatic detection of concrete cracks below the waterline","volume":"44","author":"Orinaite","year":"2022","journal-title":"Vibroeng. Procedia"}],"container-title":["Applied Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2076-3417\/14\/10\/4332\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:45:23Z","timestamp":1760107523000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2076-3417\/14\/10\/4332"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,20]]},"references-count":23,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2024,5]]}},"alternative-id":["app14104332"],"URL":"https:\/\/doi.org\/10.3390\/app14104332","relation":{},"ISSN":["2076-3417"],"issn-type":[{"type":"electronic","value":"2076-3417"}],"subject":[],"published":{"date-parts":[[2024,5,20]]}}}