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Record W4283644651 · doi:10.1680/jmacr.21.00089

Suitability of electrochemical test methods for evaluating corrosion of steel in alkali-activated slag concrete

2022· article· en· W4283644651 on OpenAlexfundno aff
Qianmin Ma, Yun Bai, Changhui Yang, Muhammed Basheer, Sreejith Nanukuttan

Bibliographic record

VenueMagazine of Concrete Research · 2022
Typearticle
Languageen
FieldEngineering
TopicConcrete Corrosion and Durability
Canadian institutionsnot available
FundersEngineering and Physical Sciences Research CouncilUniversity of LeedsChina Scholarship CouncilQueen's UniversityKunming University of Science and TechnologyQueen's University Belfast
KeywordsCorrosionGravimetric analysisPortland cementMaterials scienceMetallurgySlag (welding)CementSodium silicateElectrochemistryAlkali metalComposite materialChemistry

Abstract

fetched live from OpenAlex

The electrochemical tests commonly used for evaluating reinforcement corrosion in Portland cement-based concrete may not be appropriate for testing alkali-activated slag (AAS) concrete due to its different pore structure and pore solution composition. In this article, corrosion behaviour of the steel bars in 12 AAS concrete mixes with different alkali concentration and modulus of sodium silicate solution was monitored by using gravimetric mass loss measurement. The results obtained from other electrochemical tests were compared to the gravimetric mass loss to determine their suitability for assessing the corrosion of steel in AAS concrete. It was found that only 7.8–28% of the mass loss was accounted for by the macrocell current for AAS concretes, indicating that this type of test underestimates the corrosion of the steel in AAS concrete. The steel bars in the AAS concretes gave a much higher negative half-cell potential value, presumably due to the influence of sulfides. In summary, it has been established that the criteria for assessing the onset and progress of corrosion of steel in Portland cement concrete are not suitable for the same purpose in the range of AAS concretes studied.

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)0.011
metaresearch head score (Gemma)0.008
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 score0.131
Threshold uncertainty score0.965

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.080
GPT teacher head0.419
Teacher spread0.338 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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