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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

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Authors

Pooya Adami

Patrick B. Rodrigues

Profile image of PETER WOODS

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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