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Article: A virtual reality-based ergonomic assessment approach for human-robot collaboration workstation design in modular construction manufacturing

TitleA virtual reality-based ergonomic assessment approach for human-robot collaboration workstation design in modular construction manufacturing
Authors
KeywordsErgonomic assessment
Human-robot collaboration (HRC)
Modular construction
Virtual reality (VR)
Workstation design
Issue Date1-Mar-2025
PublisherElsevier
Citation
Advanced Engineering Informatics, 2025, v. 64 How to Cite?
AbstractModular construction is an innovative approach that shifts much construction work offsite for manufacture in a factory. By balancing flexibility and productivity, human–robot collaboration (HRC) offers significant advantages in modular construction manufacturing. To ensure human wellbeing in this scenario, it is essential to conduct holistic ergonomic evaluations of HRC workstations. This research, therefore, introduces a novel approach for evaluating ergonomics using virtual reality (VR) during the HRC workstation design phase. To demonstrate feasibility and effectiveness of the approach, we present a case study involving workstation design for building module unit window installation. The results show that the designed HRC workstations can contain both physical and cognitive ergonomic risks within acceptable ranges. This study enriches the literature on modular construction manufacturing by providing a methodological framework that integrates VR into ergonomic HRC workstation design and assesses it through a series of measures. It also provides practitioners with clear guidance for mitigating ergonomic risks during the HRC workstation design stage, thereby reducing injuries and increasing the well-being of workers.
Persistent Identifierhttp://hdl.handle.net/10722/367096
ISSN
2023 Impact Factor: 8.0
2023 SCImago Journal Rankings: 1.731

 

DC FieldValueLanguage
dc.contributor.authorFu, Yonglin-
dc.contributor.authorLu, Weisheng-
dc.contributor.authorChen, Junjie-
dc.date.accessioned2025-12-03T00:35:28Z-
dc.date.available2025-12-03T00:35:28Z-
dc.date.issued2025-03-01-
dc.identifier.citationAdvanced Engineering Informatics, 2025, v. 64-
dc.identifier.issn1474-0346-
dc.identifier.urihttp://hdl.handle.net/10722/367096-
dc.description.abstractModular construction is an innovative approach that shifts much construction work offsite for manufacture in a factory. By balancing flexibility and productivity, human–robot collaboration (HRC) offers significant advantages in modular construction manufacturing. To ensure human wellbeing in this scenario, it is essential to conduct holistic ergonomic evaluations of HRC workstations. This research, therefore, introduces a novel approach for evaluating ergonomics using virtual reality (VR) during the HRC workstation design phase. To demonstrate feasibility and effectiveness of the approach, we present a case study involving workstation design for building module unit window installation. The results show that the designed HRC workstations can contain both physical and cognitive ergonomic risks within acceptable ranges. This study enriches the literature on modular construction manufacturing by providing a methodological framework that integrates VR into ergonomic HRC workstation design and assesses it through a series of measures. It also provides practitioners with clear guidance for mitigating ergonomic risks during the HRC workstation design stage, thereby reducing injuries and increasing the well-being of workers.-
dc.languageeng-
dc.publisherElsevier-
dc.relation.ispartofAdvanced Engineering Informatics-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectErgonomic assessment-
dc.subjectHuman-robot collaboration (HRC)-
dc.subjectModular construction-
dc.subjectVirtual reality (VR)-
dc.subjectWorkstation design-
dc.titleA virtual reality-based ergonomic assessment approach for human-robot collaboration workstation design in modular construction manufacturing-
dc.typeArticle-
dc.identifier.doi10.1016/j.aei.2024.103054-
dc.identifier.scopuseid_2-s2.0-85212945384-
dc.identifier.volume64-
dc.identifier.eissn1873-5320-
dc.identifier.issnl1474-0346-

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