Level of detail, information and accuracy in building information modelling of existing and heritage buildings
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it