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Record W2062547184 · doi:10.1680/macr.14.00108

Performance of rice husk ash blended cement concretes subjected to sulfate environment

2014· article· en· W2062547184 on OpenAlexaff
Khandaker M. Anwar Hossain, Muhammed S. Anwar

Bibliographic record

VenueMagazine of Concrete Research · 2014
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsCementMaterials sciencePortland cementSulfateCorrosionPozzolanPozzolanic activityChloridePozzolanaComposite materialHuskPorosityMetallurgy

Abstract

fetched live from OpenAlex

This work assessed the performance of rice husk ash (RHA)-based ASTM type I and type V (low tricalcium aluminate (C 3 A)) blended cement concrete mixtures subjected to a mixed magnesium–sodium sulfate environment for an immersion period up to 48 months. The concrete mixtures comprised a combination of two Portland cements (type I and type V) and two RHA-blended cements with two water-to-binder ratios of 0·35 and 0·45. In addition to fresh and strength properties, X-ray diffraction, differential scanning calorimetry, mercury intrusion porosimetry and rapid chloride permeability tests were conducted on all concrete mixtures to determine phase composition, pozzolanic activity, porosity and chloride ion resistance. Deterioration of concrete due to mixed sulfate attack and corrosion of reinforcing steel were evaluated by assessing concrete weight loss and measuring corrosion potentials and polarisation resistance at periodic intervals throughout the immersion period of 48 months. Plain (type I/V) cement concretes performed better in terms of deterioration and corrosion resistance than RHA-based type I/V blended cement concrete mixtures in a mixed sulfate environment.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient 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 score0.044
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.025
GPT teacher head0.272
Teacher spread0.247 · 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

Citations13
Published2014
Admission routes1
Has abstractyes

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