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Record W2956029429 · doi:10.22260/isarc2019/0107

Application of Virtual Reality in Task Training in the Construction Manufacturing Industry

2019· article· en· W2956029429 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueProceedings of the ... ISARC · 2019
Typearticle
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsnot available
Fundersnot available
KeywordsTask (project management)Virtual realityAutomationComputer scienceManufacturingQuality (philosophy)Human–computer interactionVirtual machineManufacturing engineeringSimulationEngineeringSystems engineeringOperating system

Abstract

fetched live from OpenAlex

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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.268
Threshold uncertainty score0.215

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.212
Teacher spread0.201 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it