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Record W1982456600 · doi:10.1680/adcr.2003.15.2.83

Chloride diffusivity of volcanic ash blended hardened cement paste

2003· article· en· W1982456600 on OpenAlexaff
Khandaker M. Anwar Hossain

Bibliographic record

VenueAdvances in Cement Research · 2003
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsToronto Metropolitan University
FundersPapua New Guinea University of Technology
KeywordsMaterials scienceChlorideCementCuring (chemistry)Thermal diffusivityComposite materialDifferential scanning calorimetryMineralogyMetallurgyChemistryThermodynamics

Abstract

fetched live from OpenAlex

This paper reports the results of investigations on the chloride diffusivity of volcanic ash (VA) blended hardened cement pastes with varying curing age of up to one year. The pastes had 0, 20 and 40% VA as cement replacement by mass and water/binder ratios of 0·40, 0·50 and 0·60 by mass. The ACID test was used to calculate the chloride ion diffusion coefficient D i , of pastes using the Nernst–Plank equation for steady state conditions. In addition, electrical resistivity, mercury intrusion porosimetry, and differential scanning calorimetry (DSC) tests were also conducted. Good correlations were found among D i , total pore volume and electrical resistivity of the pastes. The D i of VA blended pastes was within the range of 10 −7 and 10 −9 cm 2 /s. It was also found that blending cement with VA significantly reduced the long-term chloride ion diffusion coefficient and hence increased the long-term corrosion resistance of pastes. This fact was also supported by the presence of lower quantity of Ca(OH) 2 and higher quantity of Friedel's salt in the VA blended pastes as observed from the DSC tests. Pastes with 40% VA showed better performance in terms of chloride ion diffusivity. W/b ratio was also found to affect the D i at the early ages of curing, but became less important at the later ages of curing.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.153
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.036
GPT teacher head0.339
Teacher spread0.303 · 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 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

Citations9
Published2003
Admission routes1
Has abstractyes

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