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Record W1876854452 · doi:10.1139/cjce-2014-0353

Chloride intrusion into thermally damaged self-compacting concrete

2015· article· en· W1876854452 on OpenAlexvenueno aff
Rami H. Haddad, Ayat Hinawi

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2015
Typearticle
Languageen
FieldEngineering
TopicFire effects on concrete materials
Canadian institutionsnot available
Fundersnot available
KeywordsDiffusionChlorideRelative humidityMaterials scienceComposite materialCompressive strengthHumidityCementWater–cement ratioIntrusionAnalytical Chemistry (journal)ChemistryThermodynamicsMetallurgyGeology

Abstract

fetched live from OpenAlex

The post-heating resistance of limestone self-compacting concrete (SCC) against chloride intrusion is investigated considering key parameters such as water-to-cement ratio (0.4, 0.45, and 0.5), relative humidity, and elevated temperature (300 °C and 400 °C). The SCC mixtures were proportioned to conform to universal specification with regard to different workability requirements. Chloride profiles were determined for post-heated and companion prismatic (100 mm × 100 mm × 250 mm) specimens, kept at room temperature. Consequently, diffusion coefficients were determined based on Fick’s steady state formula. Post-heating damage was quantified, as well, using various techniques such as ultrasonic pulse velocity waves, resonant frequency, compression test measurements. The results indicated significant reductions in compressive strength and estimated dynamic modulus ranging from 20 to 60% and 10 to 40%, respectively, with a corresponding increase in chloride diffusion coefficient reaching 80%. Both temperature and relative humidity levels had tangible impact on post-heating damage of SCC, hence percentage increase in chloride diffusion coefficient. The empirical models developed in this work showed excellent correlation between various damage indices and the percentage increase in diffusion coefficient. Furthermore, the electrical charge passing through SCC compared very well with the percentage increase in diffusion coefficient.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.154
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.008
GPT teacher head0.186
Teacher spread0.178 · 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.

Study designSimulation or modeling
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

Citations1
Published2015
Admission routes1
Has abstractyes

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