Development of Performance-Based Codes,
Bibliographic record
Abstract
This paper presents the results of a literature survey, undertaken by the National Research Council of Canada, on the efforts to move from prescriptive building regulations to performancebased regulations. This survey has revealed that, in recent years, in many countries around the world, building codes are moving from prescriptive- to performance-based requirements. This increasing world-wide tendency to move toward performance-based codes is due, in part, to the negative aspects of the prescriptive codes, to advances made in fire science and engineering, to the need for codes to use fire safety engineering principles within the context of their regulations, and to the global harmonization of regulation systems. In addition, a performance-based code approach improves the regulatory environment by establishing clear code objectives and safety criteria and leaving the means of achieving these objectives to the designer. Hence, the codes will be more flexible in allowing innovation and more functional. Performance-based codes will also permit the use of modelling tools for measuring the performance of any number of design alternatives against the established safety levels. In this way, improved fire safety designs at reduced costs might be achieved. This paper also describes the required steps for developing performance-based codes. The description outlines a set of objectives formulated based on a combination of international formulations. Also presented are some of the performance design criteria for quantifying the desired fire safety objectives and some of the existing fire safety design tools for quantifying the performance objectives. The full utilization of the existing tools in performance-based design will depend on the systems in place...
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".