Pooya Adami
Effectiveness of VR-based training on improving construction workers’ knowledge, skills, and safety behavior in robotic teleoperation
Adami, Pooya; Rodrigues, Patrick B.; Woods, Peter J.; Becerik-Gerber, Burcin; Soibelman, Lucio; Copur-Gencturk, Yasemin; Lucas, Gale
Authors
Patrick B. Rodrigues
PETER WOODS PETER.WOODS@NOTTINGHAM.AC.UK
Assistant Professor
Burcin Becerik-Gerber
Lucio Soibelman
Yasemin Copur-Gencturk
Gale Lucas
Abstract
The emergence of construction robotics and automation has produced an urgent and vast need for construction workers to reskill and upskill for the future of work. Virtual Reality (VR)-based training has been considered and investigated as a safe and cost-effective training method that allows workers to be exposed to hazardous tasks with negligible actual safety risks in comparison to existing training methods (hands-on, lecture-based, apprenticeship training). This paper aims to investigate the impact of VR-based training on construction workers’ knowledge acquisition, operational skills, and safety behavior during robotic teleoperation compared to the traditional in-person training method. Fifty construction workers were randomly assigned to complete either VR-based or in-person training for operating a demolition robot. We used quantitative and qualitative data analyses to answer our research questions. Our results indicate that VR-based training was associated with a significant increase in knowledge, operational skills, and safety behavior compared to in-person training. Our findings suggest that VR-based training not only provides a viable and effective option for future training programs but a valuable option for construction robotics safety and skill training.
Citation
Adami, P., Rodrigues, P. B., Woods, P. J., Becerik-Gerber, B., Soibelman, L., Copur-Gencturk, Y., & Lucas, G. (2021). Effectiveness of VR-based training on improving construction workers’ knowledge, skills, and safety behavior in robotic teleoperation. Advanced Engineering Informatics, 50, Article 101431. https://doi.org/10.1016/j.aei.2021.101431
Journal Article Type | Article |
---|---|
Acceptance Date | Sep 27, 2021 |
Online Publication Date | Oct 7, 2021 |
Publication Date | 2021-10 |
Deposit Date | Mar 13, 2023 |
Publicly Available Date | Mar 28, 2023 |
Journal | Advanced Engineering Informatics |
Print ISSN | 1474-0346 |
Electronic ISSN | 1474-0346 |
Publisher | Elsevier |
Peer Reviewed | Peer Reviewed |
Volume | 50 |
Article Number | 101431 |
DOI | https://doi.org/10.1016/j.aei.2021.101431 |
Keywords | Artificial Intelligence; Information Systems; Building and Construction |
Public URL | https://nottingham-repository.worktribe.com/output/18504204 |
Publisher URL | https://www.sciencedirect.com/science/article/pii/S147403462100183X?via%3Dihub |
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