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Record W7001342025

Integrating fire resistance into infrastructure projects

2005· article· en· W7001342025 on OpenAlexvenueno aff

Bibliographic record

VenueNPARC · 2005
Typearticle
Languageen
FieldComputer Science
TopicData Analysis with R
Canadian institutionsnot available
Fundersnot available
KeywordsFire resistanceFire protectionFire safetyResistance (ecology)Critical infrastructureFirefighting
DOInot available

Abstract

fetched live from OpenAlex

Fire represents one of the most severe environmental conditions to which structures may be subjected and hence, the provision of appropriate fire safety measures for structural members is an important aspect of design. While the fire resistance provisions are one of the major requirements in buildings, the need for extending such provisions to infrastructure is being debated after some of the recent fire incidents in tunnels and bridges. Also, the wider use of high-performing materials (HPM), such as high-strength concrete (HSC) and fibre-reinforced polymers (FRP) in infrastructure applications, has brought fire issues t the forefront of this debate. In the paper, the different fire scenarios associated with infrastructure projects will be explained. The complexities associated with the fire resistance performance of HPM, mainly HSC and FRP, will be outlined. An approach will be suggested to integrate fire safety into overall structural design. Through case studies, it will be illustrated that cost-effective design can be achieved by integrating fire resistance into the overall design.

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 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: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.676
Threshold uncertainty score0.426

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.008
GPT teacher head0.239
Teacher spread0.232 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

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".

Quick stats

Citations0
Published2005
Admission routes1
Has abstractyes

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