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Effect of Stray Current on Cement-Based Materials under Sulfate Attack

2021· article· en· W3215989011 on OpenAlexaff
Gaonian Li, Baomin Wang, Daman K. Panesar

Bibliographic record

VenueJournal of Materials in Civil Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicConcrete Corrosion and Durability
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsStray voltageSulfateGypsumEttringiteCurrent (fluid)CementMaterials scienceComposite materialMetallurgyCorrosionGeologyPortland cement

Abstract

fetched live from OpenAlex

In coastal areas, concrete of subway infrastructure can be jeopardized by coupled degradation conditions of stray current and sulfate solution. This paper focuses on the effect of stray current on the durability of cement-based materials exposed to sulfate-rich environments. In addition to chemical analysis, mechanical, transport, and microstructural properties of specimens exposed to five conditions were analyzed: (1) control environment with no degradation conditions, (2) stray current, (3) internal mixing sulfate, (4) stray current and internal mixing sulfate, and (5) stray current and external sulfate solution. Experimental results of this study reveal that (1) stray current can exacerbate leaching of cement-based materials, and 40-V stray current results in greater leaching compared to specimens subjected to 20-V stray current; (2) compared to specimens subjected to internal mixing sulfate alone, the combined effect of stray current on the production of gypsum and ettringite is slight due to the increase in particle collision induced by stray current; and (3) stray current has a migration effect on the rate of SO42− ions ingress, which significantly aggravates the external sulfate attack on cement-based materials. The primary degradation products were ettringite and gypsum, which results in the cracking and the maximum decrease in strength of 47.1% after exposure to coupled conditions of 40-V stray current and external sulfate solution for 150 days.

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)0.001
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 score0.235
Threshold uncertainty score0.856

Codex and Gemma teacher scores by category

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

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 head0.015
GPT teacher head0.262
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.

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

Citations13
Published2021
Admission routes1
Has abstractyes

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