Establishing an Appropriate Level of Detail (LoD) for a Building Information Model (BIM) – West Block, Parliament Hill, Ottawa, Canada
Bibliographic record
Abstract
Abstract. In 2011, Public Works and Government Services Canada (PWGSC) embarked on a comprehensive rehabilitation of the historically significant West Block of Canada’s Parliament Hill. With over 17 thousand square meters of floor space, the West Block is one of the largest projects of its kind in the world. As part of the rehabilitation, PWGSC is working with the Carleton Immersive Media Studio (CIMS) to develop a building information model (BIM) that can serve as maintenance and life-cycle management tool once construction is completed. The scale and complexity of the model have presented many challenges. One of these challenges is determining appropriate levels of detail (LoD). While still a matter of debate in the development of international BIM standards, LoD is further complicated in the context of heritage buildings because we must reconcile the LoD of the BIM with that used in the documentation process (terrestrial laser scan and photogrammetric survey data). In this paper, we will discuss our work to date on establishing appropriate LoD within the West Block BIM that will best serve the end use. To facilitate this, we have developed a single parametric model for gothic pointed arches that can be used for over seventy-five unique window types present in the West Block. Using the AEC (CAN) BIM as a reference, we have developed a workflow to test each of these window types at three distinct levels of detail. We have found that the parametric Gothic arch significantly reduces the amount of time necessary to develop scenarios to test appropriate LoD.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".