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Record W2332695980 · doi:10.1177/1042391513484911

Twenty years of performance-based fire protection design: challenges faced and a look ahead

2013· article· en· W2332695980 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Fire Protection Engineering · 2013
Typearticle
Languageen
FieldEngineering
TopicFire dynamics and safety research
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsProcess (computing)Context (archaeology)Engineering design processEngineeringPerspective (graphical)Risk analysis (engineering)Key (lock)Construction engineeringSystems engineeringArchitectural engineeringComputer scienceManagement scienceComputer securityBusiness

Abstract

fetched live from OpenAlex

A review of two decades of worldwide experience using standards, codes and guidelines related to performance-based fire protection design for buildings has identified shortcomings in the interpretation, application and implementation of the performance-based design process, apparent inconsistency in the resulting levels of performance achieved and several opportunities to enhance the process. In a constantly evolving building environment, technical challenges have to be overcome because fire safety engineering still depends greatly on knowledge gained from scientific and engineering research across a broad range of disciplines (e.g., better understanding of the fire phenomena, the behavior and response of the building occupants/contents/structure to the fire, tools for engineering analysis and all the necessary data needed to support tool application). Political challenges also need to be considered as performance-based fire protection design requires the approval of the authority having jurisdiction and other involved stakeholders, at several of its different steps (design, construction, original usage, modifications of usage). The review presented here has been undertaken from an engineering perspective rather than a regulatory perspective. Two key outcomes of this engineering review are that several of the challenges that have been identified are strongly linked to the application of generic guidance to specific problems, which results in critical details being missed, and that some of the engineering issues are treated within a political context, while they should be addressed as purely technical issues.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.374
Threshold uncertainty score0.574

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.025
GPT teacher head0.204
Teacher spread0.180 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it