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dc.contributor.authorDel Savio, Alexandre Almeidaes_PE
dc.contributor.authorCárdenas Salas, Daniel Enriquees_PE
dc.contributor.authorLuna Torres, Ana Felícitaes_PE
dc.contributor.authorVergara Olivera, Mónica Alejandraes_PE
dc.contributor.authorUrday Ibarra, Gianella Taniaes_PE
dc.contributor.otherDel Savio, Alexandre Almeidaes_PE
dc.contributor.otherCárdenas Salas, Daniel Enriquees_PE
dc.contributor.otherLuna Torres, Ana Felícitaes_PE
dc.contributor.otherVergara Olivera, Mónica Alejandraes_PE
dc.date.accessioned2023-10-09T17:16:57Z
dc.date.available2023-10-09T17:16:57Z
dc.date.issued2023
dc.identifier.citationDel Savio, A. A., Luna Torres, A., Cárdenas Salas, D., Vergara Olivera, M. A. & Urday Ibarra, G. T. (2023). Detection and Evaluation of Construction Cracks through Image Analysis Using Computer Vision. Applied Sciences, 13(17). https://doi.org/10.3390/app13179662es_PE
dc.identifier.issn2076-3417
dc.identifier.urihttps://hdl.handle.net/20.500.12724/19064
dc.description.abstractThe introduction of artificial intelligence methods and techniques in the construction industry has fostered innovation and constant improvement in the automation of monitoring and control processes at construction sites, although there are areas where more studies still need to be conducted. This paper proposes a method to determine the criticality of cracks in concrete samples. The proposed method uses a previously trained YOLOv4 neural network to identify concrete cracks. Then, the region of interest, determined by the bounding box resulting from the neural network model classification, is extracted. Finally, the extracted image is converted to negative grayscale to quantify the number of white pixels above a certain threshold, automatically allowing the system to characterize the fracture’s extent and criticality. The classification module reached a veracity between 98.36% and 99.75% when identifying five concrete crack types of failures in 1132 images. A qualitative analysis of the results obtained from the characterization module shows a promising alternative to evaluate the criticality of concrete cracks. © 2023 by the authors.es_PE
dc.formatapplication/html
dc.language.isoenges_PE
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)es_PE
dc.relation.ispartofurn:issn: 2076-3417
dc.rightsinfo:eu-repo/semantics/openAccesses_PE
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/*
dc.sourceRepositorio Institucional - Ulimaes_PE
dc.sourceUniversidad de Limaes_PE
dc.subjectPendientees_PE
dc.subject.classificationPendiente / Pendientees_PE
dc.titleDetection and Evaluation of Construction Cracks through Image Analysis Using Computer Visiones_PE
dc.typeinfo:eu-repo/semantics/articlees_PE
dc.type.otherArtículo en Scopuses_PE
dc.identifier.journalApplied Scienceses_PE
dc.publisher.countryCHes_PE
dc.description.peer-reviewRevisión por pareses_PE
dc.subject.ocdehttps://purl.org/pe-repo/ocde/ford#2.01.00es_PE
dc.identifier.doihttps://doi.org/10.3390/app13179662
dc.type.versioninfo:eu-repo/semantics/publishedVersiones_PE
ulima.cat
ulima.autor.afiliacionDel Savio, Alexandre Almeida (Scientific Research Institute (IDIC), Universidad de Lima)es_PE
ulima.autor.afiliacionCárdenas Salas, Daniel Enrique (Scientific Research Institute (IDIC), Universidad de Lima)es_PE
ulima.autor.afiliacionLuna Torres, Ana Felícita (Scientific Research Institute (IDIC), Universidad de Lima)es_PE
ulima.autor.afiliacionVergara Olivera, Monica Alejandra (Scientific Research Institute (IDIC), Universidad de Lima)es_PE
ulima.autor.carreraDel Savio, Alexandre Almeida (Ingeniería Civil)es_PE
ulima.autor.carreraCárdenas Salas, Daniel Enrique (Ingeniería de Sistemas)es_PE
ulima.autor.carreraLuna Torres, Ana Felícita (Ingeniería Civil)es_PE
ulima.autor.carreraVergara Olivera, Mónica Alejandra (Ingeniería Civil)es_PE


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