Case Study of Constructability Reasoning in MEP Coordination
Bibliographic record
Abstract
Building system coordination is a complicated process that requires the detailed layout and configuration of the various building systems such that it complies with design, construction, and operations criteria. Current practice involves an iterative process of sequentially overlaying transparent 2D drawings of each system over a light table to identify potential conflicts and constraints, which is a time-consuming and error-prone process. Recent research efforts have focused on the development of knowledge-based systems to further assist this coordination process. The objectives of this research were to collect and classify data on constructability knowledge utilized as part of a 3D MEP coordination process during design and construction of a complex research facility. We worked with the project team to develop detailed 3D models of all the building systems in several critical spaces. We identified the design and construction knowledge utilized to coordinate these 3D models and classified this knowledge in a multi-variable knowledge framework. The representational framework, 3D modeling process, and modeling constraints are discussed.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".