Investigating the Impact of Augmented Reality and BIM on Retrofitting Training for Non-Experts

Augmented Reality (AR) tools have shown significant potential in providing on-site visualization of Building Information Modeling (BIM) data and models for supporting construction evaluation, inspection, and guidance. Retrofitting existing buildings, however, remains a challenging task requiring mor...

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Veröffentlicht in:IEEE transactions on visualization and computer graphics. - 1996. - 29(2023), 11 vom: 03. Nov., Seite 4655-4665
1. Verfasser: Sermarini, John (VerfasserIn)
Weitere Verfasser: Michlowitz, Robert A, LaViola, Joseph J, Walters, Lori C, Azevedo, Roger, Kider, Joseph T
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2023
Zugriff auf das übergeordnete Werk:IEEE transactions on visualization and computer graphics
Schlagworte:Journal Article
Beschreibung
Zusammenfassung:Augmented Reality (AR) tools have shown significant potential in providing on-site visualization of Building Information Modeling (BIM) data and models for supporting construction evaluation, inspection, and guidance. Retrofitting existing buildings, however, remains a challenging task requiring more innovative solutions to successfully integrate AR and BIM. This study aims to investigate the impact of AR+BIM technology on the retrofitting training process and assess the potential for future on-site usage. We conducted a study with 64 non-expert participants, who were asked to perform a common retrofitting procedure of an electrical outlet installation using either an AR+BIM system or a standard printed blueprint documentation set. Our findings indicate that AR+BIM reduced task time significantly and improved performance consistency across participants, while also decreasing the physical and cognitive demands of the training. This study provides a foundation for augmenting future retrofitting construction research that can extend the use of [Formula: see text] technology, thus facilitating more efficient retrofitting of existing buildings. A video presentation of this article and all supplemental materials are available at https://github.com/DesignLabUCF/SENSEable_RetrofittingTraining
Beschreibung:Date Revised 06.11.2023
published: Print-Electronic
Citation Status PubMed-not-MEDLINE
ISSN:1941-0506
DOI:10.1109/TVCG.2023.3320223