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...
Veröffentlicht in: | IEEE transactions on visualization and computer graphics. - 1996. - 29(2023), 11 vom: 03. Nov., Seite 4655-4665 |
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Weitere Verfasser: | , , , , |
Format: | Online-Aufsatz |
Sprache: | English |
Veröffentlicht: |
2023
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Zugriff auf das übergeordnete Werk: | IEEE transactions on visualization and computer graphics |
Schlagworte: | Journal Article |
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 |
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Beschreibung: | Date Revised 06.11.2023 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
ISSN: | 1941-0506 |
DOI: | 10.1109/TVCG.2023.3320223 |