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The integration of robots, particularly drones, into future construction sites introduces new safety challenges requiring enhanced situational awareness (SA) among workers. To address these challenges, this study explores the effectiveness of an AI-driven assistant designed to inform workers about dynamic environmental changes via auditory and visual channels. A mixed-reality bricklaying experiment was developed, simulating worker-drone interactions across three interaction levels: coexistence, cooperation, and collaboration. One hundred five construction-background students participated in tasks with and without the AI assistant, during which their eye-tracking data, productivity, and subjective perceptions were collected. Results indicated that the AI assistant significantly expedited workers’ awareness of approaching drones but concurrently reduced bricklaying productivity. Although participants reported high perceived usefulness and low distraction by the AI assistant itself, findings revealed a trade-off: improved SA toward drones came at the cost of decreased task performance, likely due to increased attentional shifts toward drones. Furthermore, the effectiveness of the assistant varied depending on the interaction level with drones. This study highlights both the opportunities and challenges of applying AI-driven informational systems in future construction environments, offering critical insights for designing human-centered AI technologies that balance safety enhancement with productivity maintenance.
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Towards Intelligent Agents to Assist in Modular Construction: Evaluation of Datasets Generated in Virtual Environments for AI training
Modular construction aims at overcoming challenges faced by the traditional construction process such as the shortage of skilled workers, fast-track project requirements, and cost associated with on-site productivity losses and recurrent rework. Since manufacturing is done off-site in controlled factory settings, modular construction is associated with increased productivity and better quality control. However, because every construction project is unique and results in distinct work pieces and building elements to be assembled, modular construction factories necessitate better mechanisms to assist workers during the assembly process in order to minimize errors in selecting the pieces to be assembled and idle times while figuring out the next step in an assembly sequence. Machine intelligence provides opportunities for such assistance; however, a challenge is to rapidly generate large datasets with rich contextual data to train such intelligent agents. This work overviews a mechanism to generate such datasets in virtual environments and evaluates the performance of AI models trained using data generated in virtual environments in recognizing the next installation step in modular assembly sequences. Performance of the trained MV-CNN models (with accuracy of 0.97) shows that virtual environments can potentially be used to generate the required datasets for AI without the costly, time-consuming, and labor-intensive investments needed upfront for capturing real-world data.
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- Award ID(s):
- 2036870
- PAR ID:
- 10388386
- Date Published:
- Journal Name:
- Proceedings of the ISARC
- ISSN:
- 2413-5844
- Page Range / eLocation ID:
- 327-333
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.22260/ISARC2021/0046
