Leticia M. G. Morais 
, Heitor C. Dantas, Paulo H. A. Bezerra , Ana Cláudia Souza Vidal de Negreiros & Rosana C. B. Rego
Abstract: Detecting civil construction defects, such as material deterioration and cracks on masonry or structural members, is crucial for building safety and durability. Among all defects, cracks – whether visible or hidden – are critical issues that can compromise the integrity of buildings, bridges, roads, and other infrastructure elements. Artificial intelligence (AI) approaches, such as deep learning algorithms, can assist in the early identification and characterization of cracks, facilitating preventative actions to avoid future problems. In this study, we explore the application of deep learning with image segmentation techniques for crack detection and characterization in civil construction elements. We implemented a residual neural network capable of detecting cracks either in isolation or by mapping their distribution across surfaces such as concrete, bricks, steel, and wood. Additionally, we integrated the segmentation model SAM to improve the precision of crack segmentation in images. Through simulations and comparative analysis, we evaluated the performance of the models in accurately identifying and delineating cracks in civil infrastructure. The proposed model achieved an accuracy of 100\% and an intersection over union of 0.95. Despite these high-performance metrics, there remains room for error analysis to further refine the approach, particularly in complex or edge-case scenarios. These results demonstrate the efficacy of the proposed approach in achieving accurate crack detection.
Keywords: Deep learning, crack detection, artificial intelligence, civil engineering, image segmentation.
DOI code: 10.21528/lnlm-vol23-no2-art4
PDF file: vol23-no2-art4.pdf
BibTex file: vol23-no2-art4.bib