Application of Virtual Reality in Task Training in the Construction Manufacturing Industry
Bibliographic record
Abstract
Application of Virtual Reality in Task Training in the Construction Manufacturing Industry Regina Barkokebas, Chelsea Ritter, Val Sirbu, Xinming Li and Mohamed Al-Hussein Pages 796-803 (2019 Proceedings of the 36th ISARC, Banff, Canada, ISBN 978-952-69524-0-6, ISSN 2413-5844) Abstract: Automation in construction manufacturing is becoming increasingly common due to the drive for higher productivity and increased quality. One important consideration in the implementation of automation is the training and maintenance of the equipment. This study proposes an approach to assess the training for assembly/disassembly and maintenance of machines developed for the construction manufacturing industry by using immersive virtual reality (VR). The application of VR allows the collection of data such as the time required to complete the task, the distance travelled, the identification of ergonomic risks (e.g., awkward body posture), and the layout effectiveness, as well as the observation of multiple users performing an identical task under laboratory circumstances. Moreover, VR significantly reduces the costs associated with real mock-ups and the time required for implementation as it allows testing machine designs in a virtual environment that mimics the machine's real operation setting. To demonstrate the proposed approach, a case study (i.e., VR experiment) is conducted. The primary objective of the case study is to use VR to assess the effectiveness of training using the VR environment for maintenance, and the complexity of the task (i.e., the amount of time needed to understand the task). The VR experiment is performed inside an office room dedicated exclusively for that purpose where participants can move freely, and interactions with the virtual environment are possible through the utilization of a headset and wireless controllers. During the experiment, information is collected both by manual observation and automatic extraction of data from the computer. Based on the analyses of the data collected, the average time to complete the task is determined, and potential areas of design improvement are identified. Keywords: Virtual Reality; VR; Maintenance; Automation; Training; Construction; Manufacturing; DOI: https://doi.org/10.22260/ISARC2019/0107 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".