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Current Status and Future Directions of Deep Learning Applications for Safety Management in Construction

Author

Listed:
  • Hieu T. T. L. Pham

    (Department of Civil and Environmental Engineering, Seoul Campus, College of Engineering, Hanyang University, Seoul 04763, Korea)

  • Mahdi Rafieizonooz

    (Department of Civil and Environmental Engineering, Seoul Campus, College of Engineering, Hanyang University, Seoul 04763, Korea)

  • SangUk Han

    (Department of Civil and Environmental Engineering, Seoul Campus, College of Engineering, Hanyang University, Seoul 04763, Korea)

  • Dong-Eun Lee

    (Department of Architectural Engineering, School of Architectural, Civil, Environmental and Energy Engineering, Kyungpook National University, Daegu 41566, Korea)

Abstract

The application of deep learning (DL) for solving construction safety issues has achieved remarkable results in recent years that are superior to traditional methods. However, there is limited literature examining the links between DL and safety management and highlighting the contributions of DL studies in practice. Thus, this study aims to synthesize the current status of DL studies on construction safety and outline practical challenges and future opportunities. A total of 66 influential construction safety articles were analyzed from a technical aspect, such as convolutional neural networks, recurrent neural networks, and general neural networks. In the context of safety management, three main research directions were identified: utilizing DL for behaviors, physical conditions, and management issues. Overall, applying DL can resolve important safety challenges with high reliability; therein the CNN-based method and behaviors were the most applied directions with percentages of 75% and 67%, respectively. Based on the review findings, three future opportunities aiming to address the corresponding limitations were proposed: expanding a comprehensive dataset, improving technical restrictions due to occlusions, and identifying individuals who performed unsafe behaviors. This review thus may allow the identification of key areas and future directions where further research efforts need to be made with priority.

Suggested Citation

  • Hieu T. T. L. Pham & Mahdi Rafieizonooz & SangUk Han & Dong-Eun Lee, 2021. "Current Status and Future Directions of Deep Learning Applications for Safety Management in Construction," Sustainability, MDPI, vol. 13(24), pages 1-37, December.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:24:p:13579-:d:697845
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    References listed on IDEAS

    as
    1. Xu, Zhaoyi & Saleh, Joseph Homer, 2021. "Machine learning for reliability engineering and safety applications: Review of current status and future opportunities," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
    2. Zhang, Limao & Wu, Xianguo & Skibniewski, Miroslaw J. & Zhong, Jingbing & Lu, Yujie, 2014. "Bayesian-network-based safety risk analysis in construction projects," Reliability Engineering and System Safety, Elsevier, vol. 131(C), pages 29-39.
    3. Chen, Huazhou & Chen, An & Xu, Lili & Xie, Hai & Qiao, Hanli & Lin, Qinyong & Cai, Ken, 2020. "A deep learning CNN architecture applied in smart near-infrared analysis of water pollution for agricultural irrigation resources," Agricultural Water Management, Elsevier, vol. 240(C).
    4. Jinzhu Lu & Lijuan Tan & Huanyu Jiang, 2021. "Review on Convolutional Neural Network (CNN) Applied to Plant Leaf Disease Classification," Agriculture, MDPI, vol. 11(8), pages 1-18, July.
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