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Afful, A E; Wang, C C; Sunindijo, R Y; Frimpong, S; Boadu, E; Baah, B (2025) Evolving job roles of women in the construction industry. Journal of Construction Engineering and Management, 151(3).

Alothman, A; Vilventhan, A (2025) Development of building information model-enabled facility management for metro rail stations. Journal of Construction Engineering and Management, 151(3).

Bae, J; Choi, B; Krupka, E; Lee, S (2025) Harnessing project identity and safety norms to promote construction workers' safety behavior: Field intervention study. Journal of Construction Engineering and Management, 151(3).

Chen, Q; Long, D; Wang, S; Chen, Q; Yuan, B (2025) Real-time detection of personal protective equipment violations for construction workers using semisupervised learning and video clips. Journal of Construction Engineering and Management, 151(3).

Fan, C L (2025) Evaluation model for crack detection with deep learning: Improved confusion matrix based on linear features. Journal of Construction Engineering and Management, 151(3).

  • Type: Journal Article
  • Keywords: concrete crack; damage detection; deep learning; image recognition
  • ISBN/ISSN: 0733-9364
  • URL: https://doi.org/10.1061/JCEMD4.COENG-14976
  • Abstract:
    Damage due to cracking can be detected through either manual visual methods or machine vision techniques for early prevention and maintenance. In recent years, image-based deep learning methods have emerged as potent tools for automatic crack detection. In this study, five deep learning object detection algorithms-faster R-CNN, single-shot detector (SSD), You Only Look Once (YOLO) v3 and v8, and RetinaNet-were systematically compared, and the results were analyzed. Object detection involves the generation of bounding boxes of various sizes for objects of interest. Because cracks are thin and small and thus difficult to capture in a unique bounding box, redundant measurements are common, but they compromise the accuracy and consistency of the model. Therefore, an improved confusion matrix based on linear features was employed in this study to evaluate the crack detection performance of the five object detection algorithms. In evaluation experiments, the overall accuracy levels of SSD were 90.6% on visible atmospherically resistant index (VARI) images, indicating effective concrete crack detection performance. Notably, SSD excels in cases involving small cracks and data imbalance, thus demonstrating a high level of model stability. This comparative analysis of the performances of different deep learning algorithms in crack detection contributes to the formulation of methods for automatic damage detection.

Helaly, H; El-Rayes, K; Ignacio, E J; Joan, H J (2025) Comparison of machine-learning algorithms for estimating cost of conventional and accelerated bridge construction methods during early design phase. Journal of Construction Engineering and Management, 151(3).

Khan, A N; Kwan, H K (2025) AI, agility, and environmental performance: A new framework for construction project managers. Journal of Construction Engineering and Management, 151(3).

Kussl, S; Wald, A; Flak, L S (2025) Change must come from within: Study of digital transformation in construction client organizations. Journal of Construction Engineering and Management, 151(3).

Li, J; Chen, Y; Wang, H (2025) Human-machine system modeling and safety risk assessment in construction operations incorporating workers' performance variability. Journal of Construction Engineering and Management, 151(3).

Liu, R; Fischer, M (2025) A context taxonomy for construction field crew members. Journal of Construction Engineering and Management, 151(3).

Liu, X; Geng, L; Liu, D; Lin, S (2025) Psychological bonding mechanisms and value creation in construction projects: Mediating role of participants' behaviors. Journal of Construction Engineering and Management, 151(3).

Luu, D T, Ng, S T and Chen, S E (2003) Parameters governing the selection of procurement system - An empirical survey. Engineering, Construction and Architectural Management, 10(3), 209–18.

Mokhtari, M; Hosseinian, S M (2025) Optimal outcome sharing among clients, builders, and designers in collaborative construction contracts: Comparing design–bid–build and design–build methods using principal–agent theory. Journal of Construction Engineering and Management, 151(3).

Nassar, K (2003) Construction contracts in a competitive market: C3M, a simulation game. Engineering, Construction and Architectural Management, 10(3), 172–8.

Rahimian, A; Ahmed, R R; Fazeli, A; Aghdam, E; Kumar Singh, A; Naji, M; Mohandes, S R; Fordjour Antwi-Afari, M; Zayed, T (2025) Examining the barriers to the adoption of IoT-based technologies in green buildings. Journal of Construction Engineering and Management, 151(3).

Shen, Z; Wu, J (2025) Multiobjective ant colony system algorithm for component-level construction schedule optimization. Journal of Construction Engineering and Management, 151(3).

Toakley, A R and Marosszeky, M (2003) Towards total project quality - A review of research needs. Engineering, Construction and Architectural Management, 10(3), 219–28.

Vásquez-Hernández, A; Alarcón, L F; Pellicer, E; Barkokebas, B (2025) Construction industrialization: A mosaic of definitions. Journal of Construction Engineering and Management, 151(3).

Wen, H; AbouRizk, S; Mohamed, Y (2025) Gap analysis of digitalization levels in construction and manufacturing: A comparative study of construction 4.0 and industry 4.0. Journal of Construction Engineering and Management, 151(3).

Wirahadikusumah, R and Abraham, D M (2003) Application of dynamic programming and simulation for sewer management. Engineering, Construction and Architectural Management, 10(3), 193–208.

Wong, T; Wei, Y; Zeng, Y; Jie, Y; Zhao, X (2025) A novel real-time torque prediction of epb shield in mixed ground using machine learning method based on geological knowledge fusion. Journal of Construction Engineering and Management, 151(3).

Yang, X; Jefferson, W; Bulbul, T (2025) Evaluation of construction worker perceptions of wearable proximity sensors during the COVID-19 pandemic. Journal of Construction Engineering and Management, 151(3).

Zhan, Z; Dong, Y; Doe, D M; Hu, Y; Li, S; Cao, S; Li, W; Han, Z (2025) Deep learning and blockchain-driven contract theory: Alleviate gender bias in construction. Journal of Construction Engineering and Management, 151(3).