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Record W3015652715 · doi:10.1680/jmacr.19.00545

Heating rate effects on the air-void network in mortars exposed to high temperatures

2020· article· en· W3015652715 on OpenAlexaff
Bingyu Xie, Muhammad Abdullah Al Mamun, Vivek Bindiganavile

Bibliographic record

VenueMagazine of Concrete Research · 2020
Typearticle
Languageen
FieldEngineering
TopicFire effects on concrete materials
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMortarMicrostructureMaterials sciencePorosityComposite materialCompressive strengthVoid (composites)Fractal dimensionFractal

Abstract

fetched live from OpenAlex

This paper examines the effect of heating rate on the microstructure of cement-based systems exposed to sustained elevated temperatures. Besides a conventional mortar designed to attain a compressive strength of 30 MPa at 28 d, a high-strength mortar was also designed to achieve a corresponding strength of 90 MPa. Samples were extracted for testing under three different heating rates, ranging from 1°C/min to 10°C/min, to a sustained elevated temperature up to 400°C. This study finds that the microstructure of high-strength mortar is more sensitive to the heating rate in comparison with that of the normal-strength mortar. The porosity of the mortar mixtures uniformly increases with an increase in the temperature of exposure. However, there was a decrease in the porosity, at any sustained elevated temperature, with an increase in the heating rate. It is seen that the fractal dimension of the pores increases with an increase in the sustained temperature. Additionally, this fractal dimension was seen to increase with an increase in the heating rate. However, this trend was more pronounced at lower temperatures of exposure, so that the rate of heating appears to become less conspicuous at higher temperatures of soaking.

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)0.003
metaresearch head score (Gemma)0.002
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 score0.021
Threshold uncertainty score0.895

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.029
GPT teacher head0.276
Teacher spread0.247 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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