Construction-Centric Building Information Modelling to Facilitate Building Panel Prefabrication
Bibliographic record
Abstract
Building information modelling (BIM) is an information technology that has the ability to change the Architecture, Engineering, and Construction (AEC) industry in terms of enhanced communication and productivity. With the rise of BIM, panelized/off-site construction is gaining momentum within the home building industry. However, BIM has not yet been leveraged to its full capability with regard to construction prefabrication, due in part to the fact that BIM models are only roughly designed by architects and engineers. Furthermore, some construction-centric information, such as drywall and sheathing layouts, which require substantial manual modelling efforts, is not presented explicitly in the BIM model. In this regard, this research exploits a BIMbased automatic approach for designing and modelling drywall and sheathing layouts in order to facilitate building panel production in the prefabrication plant. The prototype system is built on the basis of Autodesk Revit through the use of Application Programming Interface (API). This system, taking architectural BIM information as the input, generates a detailed constructioncentric BIM model in accordance with construction specifications, and further enables users to obtain shop drawings and a thorough quantity take-off and cut list in order to manage the plant production. In addition, the prototype system is capable of optimizing drywall and sheathing layout design with the objective of minimizing material waste and joint length. A case study of a wood-framed residential building is adopted to demonstrate the developed prototype system.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".