Automatic Generation of BIM Using Parametric Family
Bibliographic record
Abstract
Automatic Generation of BIM Using Parametric Family J. Lee, Y. Liu, J. Kang, S. Ahn Pages 1157-1163 (2013 Proceedings of the 30th ISARC, Montréal, Canada, ISBN 978-1-62993-294-1, ISSN 2413-5844) Abstract: A monolithic caisson is one of the major structural components commonly used in the construction of docks, breakwaters, and container terminal quay walls. Since the structure of monolithic caissons is typical and somewhat simple, its design process is also typical and repetitive, but still time consuming. To reduce the total time and enhance productivity, we developed a parametric model in Revit to automatically generate a 3D model of a monolithic caisson. It is designed to generate the Revit model of a caisson with concrete body and reinforcing bars. The size of caisson, the number of reinforcing bars, and the space between reinforcing bars are automatically changed when users update parameters. This paper presents how we developed this parametric family in Revit for monolithic caisson design and how it would enhance its design process. Keywords: BIM, Parametric modeling, Revit Family, automatic generation DOI: https://doi.org/10.22260/ISARC2013/0127 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".