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

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 st...

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Bibliographic Details
Published in:Sustainability 2021-12, Vol.13 (24), p.13579
Main Authors: Pham, Hieu T. T. L., Rafieizonooz, Mahdi, Han, SangUk, Lee, Dong-Eun
Format: Article
Language:English
Subjects:
Algorithms
Construction accidents & safety
Construction industry
Content analysis
Deep learning
Injuries
Literature reviews
Neural networks
Recurrent neural networks
Research methodology
Safety
Safety management
Sustainability
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Summary: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.
ISSN:2071-1050
2071-1050
DOI:10.3390/su132413579