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Record W2921078089 · doi:10.1139/cjce-2018-0482

The effect of amino-alcohol-based corrosion inhibitors on concrete durability

2019· article· en· W2921078089 on OpenAlexvenueno aff
Congtao Sun, Mingshi Chen, Haibing Zheng, Peng Zhang, Yantao Li, Baorong Hou

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicConcrete Corrosion and Durability
Canadian institutionsnot available
Fundersnot available
KeywordsCorrosionDurabilityChloridePenetration (warfare)Scanning electron microscopeMaterials scienceComposite materialSteel barCorrosion inhibitorReinforced concreteMetallurgyEngineering

Abstract

fetched live from OpenAlex

Laboratory investigation was carried out to assess the effect of amino-alcohol-based corrosion inhibitors on concrete durability, and the inhibition mechanism in concrete was analyzed. Both admixed type and surface-applied type corrosion inhibitors were studied by chloride penetration test, saturation test, scanning electron microscope, and X-ray diffraction. The results showed that the anti-penetrability, compactness, and hydration of concrete were improved after the two types of corrosion inhibitors were applied. The use of corrosion inhibitors mitigated the penetration of chloride ions and water into concrete, which effectively delayed corrosion of the reinforcing bar. The surface-applied corrosion inhibitor showed better efficiency in the concrete specimens compared to the admixed type. Moreover, there was a better synergy between the two agents and the best performance when the two agents were applied to the concrete simultaneously.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
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 head0.005
GPT teacher head0.181
Teacher spread0.177 · 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 source (direct Gemma or distilled Codex), 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

Citations9
Published2019
Admission routes1
Has abstractyes

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