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Record W4391482388 · doi:10.1016/j.jobe.2024.108647

An OpenBIM-based 4D approach to support coordination meetings in virtual reality environments

2024· article· en· W4391482388 on OpenAlex
Aissa Khorchi, Conrad Boton

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Building Engineering · 2024
Typearticle
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsVirtual realityVisualizationComputer scienceHuman–computer interactionScheduleBuilding information modelingVirtual machineEngineeringScheduling (production processes)Artificial intelligence

Abstract

fetched live from OpenAlex

Notwithstanding the emergence of Virtual Reality technologies, the solutions available on the market for integrating 4D and Virtual Reality are not well adapted to the principles of BIM and only propose certain visualization functionalities. Indeed, generally, such solutions are mainly simple visualization tools which do not offer BIM-based collaborative tools such as BIM Collaboration Format (BCF)-based exchanges. Our research aims to develop a method and subsequent prototype to integrate an IFC-compliant 3D model and a planning schedule in order to perform a 4D simulation in a Virtual Reality environment having BCF-based information exchange capabilities. The prototype developed achieved its theoretical and technical objectives, namely, to validate our hypothesis that the BCF can be implemented in a VR environment and support 4D simulation-based collaboration. The proposed method allows to identify some lessons learned, in terms of success factors as well as areas for improvement.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.690
Threshold uncertainty score0.504

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 head0.010
GPT teacher head0.234
Teacher spread0.224 · 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