Detection and Evaluation of Construction Cracks through Image Analysis Using Computer Vision

• Autores: Del Savio, Alexandre Almeida, Cárdenas Salas, Daniel Enrique, Luna Torres, Ana Felícita, Vergara Olivera, Mónica Alejandra, Urday Ibarra, Gianella Tania
• Formato: artículo
• Fecha de Publicación: 2023
• Institución: Universidad de Lima
• Repositorio: ULIMA-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.ulima.edu.pe:20.500.12724/19064
• Enlace del recurso: https://hdl.handle.net/20.500.12724/19064; https://doi.org/10.3390/app13179662
• Nivel de acceso: acceso abierto
• Materia: Image processing; Fracture mechanics; Building inspection; Construction industry; Artificial intelligence; Machine learning; Deep learning (Machine learning); Neural networks (Computer science); Computer vision; Structural failures; Concrete construction; https://purl.org/pe-repo/ocde/ford#2.01.00

The 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.