Gamified Virtual Reality for Building Material Reuse Planning
Bibliographic record
Abstract
Material reuse offers the construction industry a chance to massively reduce the natural resources it consumes and the amount of waste it sends to landfills. However, adopting a “circular economy” of building materials requires many new workflows. In this paper, we present and test a virtual reality approach for building material reuse planning based on reality capture and gamified virtual reality (VR). Using 3D data captured from real assets, we develop a simulation where users deconstruct and rebuild structures in an engaging, realistic, and risk-free environment. Users simultaneously conduct deconstruction planning and reuse-based architectural design. We conduct a workshop to test the simulation with an audience of multi-disciplinary professors, students, and non-academics. We use qualitative observation and surveys to observe the way that users of different demographics engage and react to the simulation. In particular, users either struggle or excel with the lack of structured objectives in the simulation. Additionally, users disagree on whether the simulation should be more technical and precise or more intuitive and creative. We propose that future simulations can increase material reuse by empowering project planners to experiment and imagine effective configurations.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".