Predicting characteristics of cracks in concrete structure using convolutional neural network and image processing

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dc.contributor.authorQayyum, Waqas
dc.contributor.authorEhtisham, Rana
dc.contributor.authorBahrami, Alireza
dc.contributor.authorMir, Junaid
dc.contributor.authorKhan, Qaiser Uz Zaman
dc.contributor.authorAhmad, Afaq
dc.contributor.authorOzkilic, Yasin Onuralp
dc.date.accessioned2024-02-23T14:34:58Z
dc.date.available2024-02-23T14:34:58Z
dc.date.issued2023
dc.departmentNEÜen_US
dc.description.abstractThe degradation of infrastructures such as bridges, highways, buildings, and dams has been accelerated due to environmental and loading consequences. The most popular method for inspecting existing concrete structures has been visual inspection. Inspectors assess defects visually based on their engineering expertise, competence, and experience. This method, however, is subjective, tiresome, inefficient, and constrained by the requirement for access to multiple components of complex structures. The angle, width, and length of the crack allow us to figure out the cause of the propagation and extent of the damage, and rehabilitation can be suggested based on them. This research proposes an algorithm based on a pre-trained convolutional neural network (CNN) and image processing (IP) to obtain the crack angle, width, endpoint length, and actual path length in a concrete structure. The results show low relative errors of 2.19%, 14.88%, and 1.11%, respectively for the crack angle, width, and endpoint length from the CNN and IP methods developed in this research. The actual path length is found to be 14.69% greater than the crack endpoint length. When calculating the crack length, it is crucial to consider its irregular shape and the likelihood that its actual path length will be greater than the direct distance between the endpoints. This study suggests measurement methods that precisely consider the crack shape to estimate its actual path length.en_US
dc.identifier.doi10.3389/fmats.2023.1210543
dc.identifier.issn2296-8016
dc.identifier.scopus2-s2.0-85165179078en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.3389/fmats.2023.1210543
dc.identifier.urihttps://hdl.handle.net/20.500.12452/15834
dc.identifier.volume10en_US
dc.identifier.wosWOS:001033601200001en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherFrontiers Media Saen_US
dc.relation.ispartofFrontiers In Materialsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectConcreteen_US
dc.subjectConvolutional Neural Networken_US
dc.subjectImage Processingen_US
dc.subjectCrack Angleen_US
dc.subjectCrack Widthen_US
dc.subjectCrack Lengthen_US
dc.titlePredicting characteristics of cracks in concrete structure using convolutional neural network and image processingen_US
dc.typeArticleen_US

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