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Record W4409502037 · doi:10.5006/c2023-18920

Chloride-induced Corrosion of Reinforcing Steels Used in Concrete Structures at Various Temperatures

2023· article· en· W4409502037 on OpenAlexaff
Nafiseh Ebrahimi, Jieying Zhang, Amin Ghaziaskar

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicConcrete Corrosion and Durability
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsCorrosionMaterials scienceChlorideMetallurgyReinforced concreteComposite material

Abstract

fetched live from OpenAlex

Abstract Changes in temperature due to global warming could increase the risk of corrosion damage to critical reinforced concrete (RC) infrastructure and impose challenges to the corrosion protection of bridges exposed to extensive amounts of de-icing salt in the winter. The chloride threshold limit (CTL) of a steel reinforcing bar (rebar) indicates its corrosion resistance to chlorides. CTL is one of the governing parameters determining the time to corrosion initiation. This study presents an experimental investigation of the temperature dependency of CTL for six types of rebar, including four grades of stainless steel subjected to pitting corrosion characterized by the potentiodynamic polarization method. The temperature was an important influencing factor on the CTL of alloys. As expected, the corrosion resistance of the rebars (i.e., CTL) decreased with higher temperatures. Additionally, the temperature dependence of the CTL was found to vary significantly among the six alloys. This study suggests that the temperature variation in the atmosphere can affect the corrosion resistance of rebar materials used in concrete, hence, changing the service life of these structures. The effect of temperature on CTL of reinforcing steel materials should be further understood and considered when selecting these alloys for more extended service design of concrete structures.

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)0.000
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 score0.053
Threshold uncertainty score0.678

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.019
GPT teacher head0.239
Teacher spread0.220 · 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
Published2023
Admission routes1
Has abstractyes

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