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Record W4284965859 · doi:10.1016/j.rinma.2022.100301

An integrated investigation of the crystalline, rheological and physical properties of corrosion products in cementitious materials

2022· article· en· W4284965859 on OpenAlexafffund
I.A. Metaferia, Reza Foruzanmehr, Beatriz Martín‐Pérez

Bibliographic record

VenueResults in Materials · 2022
Typearticle
Languageen
FieldEngineering
TopicConcrete Corrosion and Durability
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCorrosionMaterials scienceRheologyLepidocrociteGoethiteMetallurgyMagnetiteCementitiousCalcium hydroxideParticle (ecology)Composite materialCementChemical engineeringChemistry

Abstract

fetched live from OpenAlex

This paper discusses the behaviour of the rheological behaviour of corrosion products (produced in the laboratory) in a high alkaline environment to understand hydraulic-induced fracture by corrosion products in reinforced concrete. A mixture of iron powder, sodium chloride, calcium hydroxide, and water was used to produce corrosion products. The development of corrosion products was studied at 2, 4, 6, and 8-week intervals. Imaging techniques, along with rheological characterization methods, were conducted to understand the viscoelastic flow behaviour of corrosion products. The results show that corrosion products exhibited different rheologic properties throughout the corrosion process. Three iron oxide phases (Magnetite, Goethite, and Lepidocrocite) were detected throughout the initiation and propagation stages of corrosion in the laboratory. The corrosion products obtained for each study period behaved as colloidal materials, demonstrating all the characteristics of shear-thinning materials. As a result of chemical and particle disintegration, the viscosity of corrosion products decreased with the applied shear rate. In addition, the experimental data showed that the density and particle size of corrosion products decreased with time. These results can be used to understand the progress of corrosion's damage in reinforced concrete.

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 score0.011
Threshold uncertainty score0.261

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
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)0.0000.000

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 head0.022
GPT teacher head0.218
Teacher spread0.196 · 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

Citations0
Published2022
Admission routes2
Has abstractyes

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