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Evaluating Sulphate Resistance of Cement-Based Systems by Sulphate Content Determination after Exposure

2016· article· en· W2522260081 on OpenAlexafffund
Vivek Bindiganavile, Chi Qian Ou, Zheng Chen, Yaman Boluk

Bibliographic record

VenueKey engineering materials · 2016
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsUniversity of Alberta
FundersDivision of Materials ResearchNatural Sciences and Engineering Research Council of Canada
KeywordsEttringiteCementFly ashMaterials scienceUltimate tensile strengthPorosityComposite materialSulfateMetallurgyPortland cement

Abstract

fetched live from OpenAlex

This paper describes approaches to evaluating the resistance of cement-based composites to sulphate attack. The conventional approach of evaluation by means of measuring expansion is discussed in comparison with the sulphate diffusion, which was quantified as a function of depth. Besides CSA Types GU and HS, a 30:70 blend of fly ash and cement Type GU was also examined. The specimens so produced were immersed in a sulphate solution as per ASTM C1012 and retrieved variously after 7, 14, 28, 56 and 84 days of exposure. As expected, Type HS cement performed best with minimum expansion and sulphate ingress. On the other hand, the Type GU cement showed lower expansion and sulphate ingress in comparison to the fly ash blended binder. Although bearing identical porosity, the blended binder had the smallest median pore size. Therefore, the sulphate ingress and consequent ettringite production likely cracks the blended system more than the other two. Significantly, after longer durations of sulphate exposure, the blended system showed higher tensile strength which implies a healing of cracks through ettringite formation.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.032
GPT teacher head0.257
Teacher spread0.225 · 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 source (direct Gemma or distilled Codex), 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

Citations5
Published2016
Admission routes2
Has abstractyes

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