Combined Application of 4D BIM Schedule and an Immersive Virtual Reality on a Modular Project: UNLV Solar Decathlon Case
Bibliographic record
Abstract
The use of a 4D schedule as technological advancement has brought significant improvement to the planning and execution of construction projects, through visualizing step-wise construction progress, following a sequence of pre-planned activities, and finalizing a baseline schedule with necessary changes. Moreover, the application of immersive virtual reality (IVR) to create an interactive 4D BIM schedule of a planned structure has made it possible to create a detailed plan of any construction project. Because of these benefits, the use of 4D schedules and immersive virtual reality in the construction industry has increased, leading to improved planning and execution. However, past studies have given little attention to the applications of such technologies on modular projects. Thus, this research applied a 4D schedule, along with immersive virtual reality, on a modular project, and verified their benefits and effectiveness. The results showed that most of the participants who experienced a 4D BIM schedule, along with immersive virtual reality (4D/IVR), strongly agreed that it is an easy and straightforward way to visualize the project, understand the schedule, and find any errors. Moreover, while fewer than half of the participants scheduled the assembly sequence correctly with conventional schedule and 2D drawings, almost all of them sequenced the assembly successfully with 4D/IVR. Based on the findings, this research concludes that the implementation of a 4D BIM schedule, along with virtual reality technology, can enhance the fabrication and assembly performance of modules.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".