Modeling and coordinating building systems in three dimensions: a case study
Bibliographic record
Abstract
This paper presents a case study that investigated a building system coordination process used three-dimensional (3-D) models during design and construction of a complex research facility. We modeled and coordinated a variety of building systems in three dimensions, including architectural, structural, mechanical, electrical, and plumbing systems. We documented the 3-D modeling and coordination process, evaluated existing software tools support of this process, documented the resources required to execute this process, and assessed the impact of the 3-D models on the coordination process. We also identified the design and construction knowledge used to create a coordinated and constructible design. We classified this knowledge in a framework instantiated by examples and concepts found in this study. The framework associates the design and construction constraints that govern the modeling and coordination process with the knowledge domain, the domain context, and the specific modeling and coordination task. The main contributions of the paper are the evaluation of the 3-D coordination process and the identification and classification of building system coordination knowledge.Key words: 3-D modeling, mechanical, electrical, and plumbing (MEP) coordination, building systems, knowledge framework, product and process modeling, constructability.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".