Level of detail, information and accuracy in building information modelling of existing and heritage buildings
Bibliographic record
Abstract
Purpose Over the past decade, national and international organisations concerned with regulating the architecture, engineering, construction and operations industry have been working to create guidelines for the integration of building information modelling (BIM) through the establishment of benchmarks to measure the quality and quantity of information in a given model. Until recently, these benchmarks – and BIM guidelines in general – have been developed for the design and construction of new projects, providing very little guidance for using BIM in the context of conservation and rehabilitation. The purpose of this paper is to introduce a new benchmark specific to existing and heritage buildings developed by Carleton Immersive Media Studio (CIMS). Design/methodology/approach To create the new benchmark, CIMS conducted a critical evaluation of established and emerging BIM guidelines including: Level of Development Specification 2016 (BIMFORUM), architecture, engineering and construction (Can) BIM Protocol (CanBIM), PAS 1102-2: Specification for Information Management for the Capital Delivery Phase of Construction Projects Using BIM (British Standards Institution) and Level of Accuracy Specification Guide (US Institute of Building Documentation). Findings Using the authors’ on-going work at the Parliament Hill National Historic Site in Ottawa, Canada, the CIMS created and applied a three-category system that evaluated the level of detail, information and accuracy within the building information model independently. Originality/value In this paper, the authors discuss the CIMS’ work to date and propose next steps.
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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 | 0.013 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".