A Framework for Improving Business and Technical Operations within Timber Frame Self-Build Housing Sector by Applying an Integrated VR/AR and BIM Approach
Bibliographic record
Abstract
Timber Frame Self-Build Housing Sector (TFSBS), accounts only to 7-10% of the UK’s housing construction market. This figure is significantly higher in some other contexts, e.g. 80% in Austria, 60% in Canada and 50% in the USA. With the government’s policies stepping in to encourage selfbuilding, it is essential for the companies to increase their competitiveness providing an efficient workflow and clear communication with the clients by using the latest digital technologies. End-users, who make buying decisions to self-build, often have little or no knowledge of the construction industry. Therefore, they face significant challenges in communication with professionals in terms of spatial awareness, ability to visualise technical drawings and understanding the construction process. For many architectural and construction practices in the UK’s TFSBS, the primary communication medium with clients is email. This results in an increased number of the iterations per each project lifecycle, leading to difficulties in interoperability among designers, manufacturers and builders. Virtual and Augmented Reality (VR and AR) technologies are widely acknowledged as aids for clients and professionals in a more productive interaction. Game technologies have also been recognised to be effective in resolving problems in science and business. However, designing data-rich Virtual Reality Environments (VREs) that would enhance clients’ spatial understanding from the solution spaces, simplify architect-client business/marketing communications, provide parametric customisation options, consolidate quantification, support interoperability, and leverage integration across the whole BIM process are still outstanding challenges. Business and technical key performance indicators, as well as conceptual and methodological frameworks, are developed for adopting BIM principles and emerging game-like VR and AR technologies to support TFSBS within the UK housing industry in Business and Technical Operations. This paper concludes with technical recommendations for the development of a proof of concept prototype to support the aim as mentioned above.
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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 | 0.019 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".