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Aliasgari, R, Fan, C, Li, X, Golabchi, A and Hamzeh, F (2024) MOCAP and ai-based automated physical demand analysis for workplace safety. Journal of Construction Engineering and Management, 150(07).

  • Type: Journal Article
  • Keywords: artificial intelligence techniques; automation; motion capture; physical demand analysis; rule-based expert system; workplace safety
  • ISBN/ISSN: 0733-9364
  • URL: http://doi.org/10.1061/JCEMD4.COENG-13811
  • Abstract:
    Worker safety and productivity and the factors that affect them, such as ergonomics, are essential aspects of construction projects. The application of ergonomics and the identification of the connections between workers and assigned tasks have led to a decrease in worker injuries and discomfort, beneficial effects on productivity, and a reduction in project costs. Nevertheless, workers in the construction area are often subjected to awkward body postures and repetitive motions that cause musculoskeletal disorders, in turn leading to delays in production. As a systematic and widely used procedure that generates a final document or form, physical demand analysis (PDA) assesses the health and safety of workers engaged in construction or manufacturing activities and proactively evaluates ergonomic risks. However, to gather the necessary information, traditional PDA methods require ergonomists to spend significant time observing and interviewing workers. To increase the speed and accuracy of PDA, this study focuses on developing a systematic PDA framework to automatically fill a posture-based PDA form and address the physiological aspects of task demands. In contrast to the traditional observation-based approach, the proposed framework uses a motion capture (MOCAP) system and a rule-based expert system to obtain joint angles and body segment positions in different work situations, convert the measurements to objective identification of activities and their frequencies, and then automatically populate the PDA forms. The framework is tested and validated in both laboratory and on-site environments by comparing the generated forms with PDA forms filled out by ergonomists. The results indicate that the MOCAP-/AI-based automated PDA framework successfully improves the performance of PDA in terms of accuracy, consistency, and time consumption. Ultimately, this framework can aid in the design of job tasks and work environments with the goal of promoting health, safety, and productivity in the workplace.

Chae, J, Hwang, S and Kang, Y (2024) Measuring habituation to auditory warnings using behavioral and physiological data. Journal of Construction Engineering and Management, 150(07).

Chen, H and Chan, I Y S (2024) Effects of automation and transparency on human psychophysiological states and perceived system performance in construction safety automation: An electroencephalography experiment. Journal of Construction Engineering and Management, 150(07).

Dahalan, N H, Rahman, R A, Hassan, S H and Ahmad, S W (2024) Public assessment for environmental management plan implementation: Comparative study of performance indicators of road and highway construction projects. Journal of Construction Engineering and Management, 150(07).

Deep, S, Sonkar, N, Yadav, P, Vishnoi, S, Bhoola, V and Kumar, A (2024) Factors influencing the mental health of white-collar construction workers in developing economies: Analytical study during the COVID-19 pandemic in India. Journal of Construction Engineering and Management, 150(07).

Deng, S, Ni, P, Zhu, H, Cai, Y and Pan, Y (2024) Artificial cognition to predict and explain the potential unsafe behaviors of construction workers. Journal of Construction Engineering and Management, 150(07).

Eliwa, H K, Jelodar, M B, Poshdar, M and Zavvari, A (2024) Organizational infrastructure and information and communication technology infrastructure alignment in construction organizations. Journal of Construction Engineering and Management, 150(07).

Flores, M and Mourgues, C (2024) Impact of using a formalized methodology for conflict detection based on 4D-BIM. Journal of Construction Engineering and Management, 150(07).

Huang, H, Hu, H, Xu, F and Zhang, Z (2024) Kinesiology-inspired assessment of intrusion risk based on human motion features. Journal of Construction Engineering and Management, 150(07).

King Lewis, A and Shan, Y (2024) Persistence of women in the construction industry. Journal of Construction Engineering and Management, 150(07).

Lei, T, Seo, J, Liang, K, Xu, J, Li, H, Zhou, Y, Khan, M and Heung, K H (2024) Lightweight active soft back exosuit for construction workers in lifting tasks. Journal of Construction Engineering and Management, 150(07).

Li, C Z, Wen, S, Yi, W, Wu, H and Tam, V W Y (2024) Offsite construction supply chain challenges: An integrated overview. Journal of Construction Engineering and Management, 150(07).

Liu, C, González, V A, Lee, G, Cabrera-Guerrero, G, Zou, Y and Davies, R (2024) Integrating the last planner system and immersive virtual reality: Exploring the social mechanisms produced by using LPS in projects. Journal of Construction Engineering and Management, 150(07).

Ma, Z, Liu, W, Li, C, Sang, Y, Zhang, Y, Li, G and Xu, Y (2024) Research on energy-saving control strategy of loader based on intelligent identification of working stages. Journal of Construction Engineering and Management, 150(07).

Naji, K K, Gunduz, M and Mansour, M M (2024) Development of an integrated hybrid risk assessment system for construction disputes during the preconstruction phase using the Delphi method. Journal of Construction Engineering and Management, 150(07).

Qin, L, He, Q, Fu, X, Wang, Y and Wang, G (2024) Peer effects on corporate social responsibility engagement of Chinese construction firms through board interlocking ties. Journal of Construction Engineering and Management, 150(07).

Shrestha, S, Shan, Y and Goodrum, P M (2024) Identification of best practices in project bundling for state dots using semistructured interviews. Journal of Construction Engineering and Management, 150(07).

Song, S H, Choi, J O and Cho, H (2024) Transportation-induced impact on a prefinished volumetric modular house using trailer bogie: Case study. Journal of Construction Engineering and Management, 150(07).

Tan, Y, Deng, T, Zhou, J and Zhou, Z (2024) Lidar-based automatic pavement distress detection and management using deep learning and BIM. Journal of Construction Engineering and Management, 150(07).

Tran, H, Robert, D, Gunarathna, P and Setunge, S (2024) Estimating cost of bridge closure for bridge network rehabilitation priorities. Journal of Construction Engineering and Management, 150(07).

Wei, F, Hwang, B G, Zainal, N S B and Zhu, H (2024) Trust, team effectiveness, and strategies: A comparative study between virtual and face-to-face teams. Journal of Construction Engineering and Management, 150(07).

Wu, J, Ye, Y and Du, J (2024) Autonomous drones in urban navigation: Autoencoder learning fusion for aerodynamics. Journal of Construction Engineering and Management, 150(07).

Xia, X, Xiang, P, Khanmohammadi, S, Gao, T and Arashpour, M (2024) Predicting safety accident costs in construction projects using ensemble data-driven models. Journal of Construction Engineering and Management, 150(07).

Xiang, Q, Liu, Y, Goh, Y M, Ye, G and Safiena, S (2024) Investigating the impact of hazard perception failure on construction workers' unsafe behavior: An eye-tracking and thinking-aloud approach. Journal of Construction Engineering and Management, 150(07).

Yan, D, Wang, C C and Sunindijo, R Y (2024) Framework for promoting women's career development across career stages in the construction industry. Journal of Construction Engineering and Management, 150(07).