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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Bibliographic Details
Published in:IEEE transactions on visualization and computer graphics 2023-11, Vol.29 (11), p.4655-4665
Main Authors: Sermarini, John, Michlowitz, Robert A., LaViola, Joseph J., Walters, Lori C., Azevedo, Roger, Kider, Joseph T.
Format: Article
Language:English
Subjects:
Augmented reality
BIM
Building information modeling
Buildings
Data models
Data visualization
Documentation
Electric outlets
Maintenance engineering
Onsite
Retrofitting
Task analysis
Training
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Summary: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 \text{AR}+\text{BIM} 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 .
ISSN:1077-2626
1941-0506
DOI:10.1109/TVCG.2023.3320223