MétaCan
Menu
Back to cohort

Experimental investigation on low-cement concrete at elevated temperature with preloaded conditions

2024· article· en· W4404854271 on OpenAlexaff
M. V. Rokade, David Rush, T Stratford, Luke Bisby, Neil A. Hoult

Bibliographic record

VenueJournal of Physics Conference Series · 2024
Typearticle
Languageen
FieldEngineering
TopicFire effects on concrete materials
Canadian institutionsQueen's University
Fundersnot available
KeywordsCementMaterials scienceComposite materialForensic engineeringEngineering

Abstract

fetched live from OpenAlex

Abstract Concrete is one of the most widely used construction materials globally. Experimental and numerical observations have revealed that failure of concrete structures may occur not only during the heating phase but also during the decay phase of a fire. With the global imperative to reduce CO 2 emissions from cement production, traditional concrete is increasingly being replaced with low-cement alternatives. However, there remains a lack of experimental testing regarding the effects of additional supplementary cementitious materials in concrete during and after fire. This paper presents preliminary findings from elevated temperature compressive tests conducted on concrete with three different mixes, two of which involved 40% and 50% cement replacement. The experimental programme indicates that, regardless of the mix type, internal temperatures recorded in the cylinders were minimally affected by differing mix proportions under identical heating scenarios. Additionally, the paper explores the influence of preloading on both the magnitude of peak thermal expansion and the time to reach peak thermal expansion. It is observed that a reduction in cement content results in comparatively more rapid thermal expansion. Furthermore, during the decay phase, the contraction rates are similar regardless of preloading conditions or the reduction of cement content, for identical heating and cooling scenarios.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.685

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 communication0.0000.001
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 head0.013
GPT teacher head0.221
Teacher spread0.208 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations2
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Physics Conference SeriesSame topicFire effects on concrete materialsFrench-language works237,207