MétaCan
Menu
Back to cohort
Record W2314369594 · doi:10.2472/jsms.52.1075

Effectiveness of Polymer-Modified Cement Mortar with Corrosion Inhibitor in Preventing Chloride-Induced Steel Corrosion in Concrete

2003· article· en· W2314369594 on OpenAlexaff
Minobu Aoyama, Kazuyuki Torii

Bibliographic record

VenueJournal of the Society of Materials Science Japan · 2003
Typearticle
Languageen
FieldEngineering
TopicConcrete Corrosion and Durability
Canadian institutionsQuest University Canada
Fundersnot available
KeywordsCorrosionMaterials scienceSteel barChlorideCementMortarComposite materialCorrosion inhibitorPolarization (electrochemistry)Reinforced concreteMetallurgyChemistry

Abstract

fetched live from OpenAlex

In Japan, the polymer-modified cement mortar (PCM) with the corrosion inhibitor has often been used as repair materials for deteriorated reinforced concrete structures due to the chloride-induced steel corrosion. In this study, the reinforced concrete specimens with two levels of chloride content were prepared and exposed to wet and dry cycles in the chamber for a long period to clarify the macro-cell corrosion of steel bar in the concrete. The effectiveness of the type and dosage of corrosion inhibitor, LiNO2 or Ca(NO2)2, in PCM and the degree of chipping around steel bars in repair work in preventing the steel corrosion in concrete were comparatively investigated by means of electro-chemical measurements such as linear polarization resistance and AC impedance methods.From the experimental results, it was found that PCM with both corrosion inhibitors of LiNO2 and Ca(NO2)2 had almost the same effect in preventing the steel corrosion when their NO2- molecular ratio to Cl- was constant, and that the chipping and patching work beyond steel bars was more effective in restoring the passive state of steel bars. Furthermore, both the linear polarization resistance and the AC impedance methods were very useful as a nondestructive inspection method for estimating the corrosion behavior of steel bars in repaired and unrepaired concrete with PCM.

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)0.005
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 score0.020
Threshold uncertainty score0.438

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
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 head0.013
GPT teacher head0.236
Teacher spread0.223 · 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
Published2003
Admission routes1
Has abstractyes

Explore more

Same venueJournal of the Society of Materials Science JapanSame topicConcrete Corrosion and DurabilityFrench-language works237,207