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Record W4214711641 · doi:10.3390/fire5020034

Influence of Natural Fire Development on Concrete Compressive Strength

2022· article· en· W4214711641 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

VenueFire · 2022
Typearticle
Languageen
FieldEngineering
TopicFire effects on concrete materials
Canadian institutionsWestern University
Fundersnot available
KeywordsCompressive strengthEnvironmental scienceNatural (archaeology)Work (physics)Building constructionStructural engineeringMaterials scienceEngineeringComposite materialMechanical engineeringGeology

Abstract

fetched live from OpenAlex

With increasing acceptance of performance-based design principles in the field of fire safety, it is imperative to accurately define the behaviour of materials during fire exposure. Real-world fire events, otherwise referred to as natural fires, are defined by four characteristics: heating rate, maximum temperature, exposure duration, and cooling rate. Each of these four characteristics influences concrete’s behaviour in a different manner. In this paper, the available experimental work for concrete, tested at elevated temperatures, is examined to identify the influence of the four natural fire characteristics on concrete compressive strength. This review focuses on normal strength concrete tests only, omitting parameters such as unique additives and confinement. The intent is to provide a fundamental understanding of normal strength concrete. The findings show that maximum temperature and cooling rates have a significant influence on concrete strength. Exposure duration has a moderate impact, particularly at shorter durations. Variable rates of heating have minimal influence on strength. Detailed conclusions are provided along with review limitations, practical considerations for designers, and future research needs.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.403
Threshold uncertainty score0.637

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 head0.005
GPT teacher head0.195
Teacher spread0.190 · 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