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Record W2028704342 · doi:10.1139/l03-024

Sulphate resistance of silica fume blended mortars exposed to various sulphate solutions

2003· article· en· W2028704342 on OpenAlexvenueno aff
Han-Young Moon, Seung Tae Lee, Seong-Soo Kim

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2003
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsnot available
Fundersnot available
KeywordsSilica fumeMortarMaterials scienceCementScanning electron microscopeCompressive strengthMagnesiumSodiumComposite materialPorosimetryMetallurgyPorosity

Abstract

fetched live from OpenAlex

The different performances of silica fume blended mortars placed in various sulphate-bearing exposure conditions were investigated. Experimental study was carried out on mortars immersed in 5% sodium sulphate, 5% magnesium sulphate, and mixed 5% sodium sulphate and 5% magnesium sulphate solutions for 270 d of sulphate exposure. Another variable was the replacement ratio of cement with silica fume (0, 5, 10, and 15% of the cement by weight). The results showed conclusively that silica fume should be given serious consideration for use in sulphate environments. In other words, in a sodium sulphate solution, silica fume showed a beneficial effect on mitigating compressive strength reduction and length change of mortars. However, the strength deterioration factor of the mortar specimen with some silica fume content (especially 15% of the cement by weight) was greater than that of the mortar specimen without silica fume under Mg 2+ ion oriented attack. These different sulphate deterioration mechanisms are presented in the study. Microstructural investigations such as X-ray diffraction (XRD), scanning electron microscopy (SEM), energy-dispersive X-ray analysis (EDXA), and mercury intrusion porosimetry (MIP) were also used to support the explanation for these mechanisms.Key words: sodium sulphate, magnesium sulphate, silica fume, strength deterioration factor, length change.

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 score0.398
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.015
GPT teacher head0.204
Teacher spread0.189 · 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

Citations37
Published2003
Admission routes1
Has abstractyes

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