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Al-Ghzawi, M and El-Rayes, K (2023) Optimizing the planning of airport airside expansion projects to minimize air traffic disruptions and construction cost. Journal of Construction Engineering and Management, 149(04).

Barros, B A F S and Sotelino, E D (2023) Constructability and sustainability studies in conceptual projects: A BIM-based approach. Journal of Construction Engineering and Management, 149(04).

Guo, H, Zhang, Z, Yu, R, Sun, Y and Li, H (2023) Action recognition based on 3D skeleton and LSTM for the monitoring of construction workers' safety harness usage. Journal of Construction Engineering and Management, 149(04).

  • Type: Journal Article
  • Keywords: action recognition; construction worker; deep learning; fall from height; safety harness usage; three-dimensional human skeleton
  • ISBN/ISSN: 0733-9364
  • URL: http://doi.org/10.1061/JCEMD4.COENG-12542
  • Abstract:
    Fall from height (FFH) is the most common construction accident in the construction industry, thus it is significant to monitor the use of safety harnesses, which are critical to the prevention of FFH. Sensing or computer vision technologies have been adopted to identify workers' safety harness usage. However, previous research focused mainly on whether a worker wears a safety harness rather than on whether he or she properly fixes it to a lifeline, which is vital to prevent FFH but difficult to monitor. This research establishes an action recognition method based on a three-dimensional (3D) skeleton and long short-term memory (LSTM) to aid in automatically monitoring whether safety harnesses are fixed properly on site. An indoor experiment, which considered the features of a common real construction scenario - working on scaffolding - was conducted to test the effectiveness and feasibility of the proposed method. The result shows that the method achieves an acceptable precision and recall rate and can be used to detect the incorrect use of safety harnesses by combining multiple actions. This will contribute to the prevention of FFH in practice as well as to the body of knowledge of construction safety management.

Hassan, F U, Le, T and Le, C (2023) Automated approach for digitalizing scope of work requirements to support contract management. Journal of Construction Engineering and Management, 149(04).

Jeon, J, Zhang, Y, Yang, L, Xu, X, Cai, H and Tran, D (2023) Risk breakdown matrix for risk-based inspection of transportation infrastructure projects. Journal of Construction Engineering and Management, 149(04).

Koc, K, Ekmekcioǧlu, Ö and Gurgun, A P (2023) Developing a national data-driven construction safety management framework with interpretable fatal accident prediction. Journal of Construction Engineering and Management, 149(04).

Li, Y, Ning, Y and Rowlinson, S (2023) Social control in outsourced architectural and engineering design consulting projects: Behavioral consequences and motivational mechanism. Journal of Construction Engineering and Management, 149(04).

Nigra, M and Bossink, B (2023) Cooperative learning in green building demonstration projects: Insights from 30 innovative and environmentally sustainable demonstrations around the world. Journal of Construction Engineering and Management, 149(04).

Pushpakumara, B H J, Gunasekara, M T and Gannile, Y M T D (2023) Variation of mechanical and chemical properties of old and new clay bricks. Journal of Construction Engineering and Management, 149(04).

Shiha, A and Dorra, E M (2023) Resilience index framework for the construction industry in developing countries. Journal of Construction Engineering and Management, 149(04).

Shirazi, D H and Toosi, H (2023) Deep multilayer perceptron neural network for the prediction of Iranian dam project delay risks. Journal of Construction Engineering and Management, 149(04).

Xia, N, Griffin, M A, Xie, Q and Hu, X (2023) Antecedents of workplace safety behavior: Meta-analysis in the construction industry. Journal of Construction Engineering and Management, 149(04).

Xu, W and Wang, T K (2023) Construction worker safety prediction and active warning based on computer vision and the gray absolute decision analysis method. Journal of Construction Engineering and Management, 149(04).