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Record W2611925178 · doi:10.1680/jmacr.16.00406

Exploring effects of supplementary cementitious materials in concrete exposed to physical salt attack

2017· article· en· W2611925178 on OpenAlexaff
Ahmed R. Suleiman, Moncef L. Nehdi

Bibliographic record

VenueMagazine of Concrete Research · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicBuilding materials and conservation
Canadian institutionsWestern University
Fundersnot available
KeywordsMetakaolinDurabilitySilica fumeCementitiousFly ashSulfateAlkali–silica reactionSalt (chemistry)Forensic engineeringEnvironmental scienceGeotechnical engineeringMaterials scienceComposite materialAggregate (composite)EngineeringCementMetallurgyChemistry

Abstract

fetched live from OpenAlex

It has been well established that supplementary cementitious materials (SCMs) can significantly enhance the resistance of concrete to chemical sulfate attack. However, the effect of SCMs on the durability of concrete exposed to physical salt attack is still controversial. To date, there have been only limited studies that have investigated this concrete durability issue. Therefore, the present study investigates the effects of using different types of SCMs, including silica fume, fly ash and metakaolin, in concrete subjected to environments prone to physical salt attack. Results indicate that the damage of concrete escalates as the addition level of SCMs increases. An attempt is made to delineate this problem and explain the mechanisms controlling this behaviour, which could have implications for existing design codes. The findings call for caution when SCMs are specified for concrete subjected to environments conducive to physical sulfate attack.

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)0.001
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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.166
GPT teacher head0.349
Teacher spread0.183 · 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

Citations31
Published2017
Admission routes1
Has abstractyes

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