Fire Safety in Tall Timber Building: A BIM-Based Automated Code-Checking Approach
Bibliographic record
Abstract
Fire safety regulations impose very strict requirements on building design, especially for buildings built with combustible materials. It is believed that it is possible to improve the management of these regulations with a better integration of fire protection aspects in the building information modeling (BIM) approach. A new BIM-based domain is emerging, the automated code checking, with its growing number of dedicated approaches. However, only very few of these works have been dedicated to managing the compliance to fire safety regulations in timber buildings. In this paper, the applicability to fire safety in the Canadian context is studied by constituting and executing a complete method from the regulations text through code-checking construction to result analysis. A design science approach is used to propose a code-checking method with a detailed analysis of the National Building Code of Canada (NBCC) in order to obtain the required information. The method starts by retrieving information from the regulation text, leading to a compliance check of an architectural building model. Then, the method is tested on a set of fire safety regulations and validated on a building model from a real project. The selected fire safety rules set a solid basis for further development of checking rules for the field of fire safety. This study shows that the main challenges for rule checking are the modeling standards and the elements’ required levels of detail. The implementation of the method was successful for geometrical as well as non-geometrical requirements, although further work is needed for more advanced geometrical studies, such as sprinkler or fire dampers positioning.
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.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".