ADVANCED FIRE MODELLING IN SUPPORT OF PERFORMANCE-BASED FIRE DESIGN OF TIMBER BUILDINGS
Bibliographic record
Abstract
The wood products industry is growing, as evidenced by the desire to build larger and taller buildings with timber and the start-up of new manufacturers.Despite the benefits of timber, projects are still experiencing approval resistance, namely when performance-based design is proposed to demonstrate code compliance.To gain approval, testing is often required to confirm the performance of a given product or design.While large-scale testing can always be performed, it tends to be very costly and time consuming, and can be argued to be valid only for the scenarios being tested.Fortunately, greater knowledge in fire safety engineering now allows for advanced/sophisticated fire modelling techniques, including models using the computational fluid dynamic and finite element method.This paper presents a number of advanced modelling tools and an ongoing effort to develop and harmonize a material database to be used in these models.Given the complexity of advanced modelling and the variety of input parameters and properties, an initiative is being launched at FPInnovations to generate a centralized material properties database, with the intent of making it available to the design community.Guidance on the proper use of advanced models in support of performance-based design with timber elements is also being developed.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".