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Record W3091571530 · doi:10.1680/jmacr.20.00184

Analytical and numerical approaches to model cover cracking of RC structures due to corrosion

2020· article· en· W3091571530 on OpenAlexaff
A. D. Roshan, Beatriz Martín‐Pérez, Martin Noël

Bibliographic record

VenueMagazine of Concrete Research · 2020
Typearticle
Languageen
FieldEngineering
TopicConcrete Corrosion and Durability
Canadian institutionsRoyal Canadian NavyUniversity of Ottawa
Fundersnot available
KeywordsCrackingParametric statisticsConcrete coverStructural engineeringRust (programming language)CorrosionMaterials scienceBrittlenessCover (algebra)CompressibilityMechanicsGeotechnical engineeringEngineeringMathematicsComposite materialComputer scienceMechanical engineeringPhysics

Abstract

fetched live from OpenAlex

This paper presents two different approaches to model corrosion-induced crack propagation in reinforced concrete (RC) structures. Both approaches are based on a thick-walled cylinder analogy, in which the concrete cover is subjected to the internal pressure generated by the growth of corrosion products, and both consider rust compressibility and rust diffusion into cracks. The first approach is solved numerically using finite differences to model the post-cracking softening behaviour of concrete in tension. The second approach idealises the concrete cover as either a brittle elastic or an elastoplastic material, so that it may be solved using a closed-form solution. The results obtained using each approach are compared against each other as well as against published experimental results. A parametric investigation of the influence of several variables on the results provided by the modelling approaches is also presented. The experimental data found in the literature showed reasonable agreement with predictions from the numerical and elastoplastic (analytical) models.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.839
Threshold uncertainty score0.593

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.170
GPT teacher head0.322
Teacher spread0.151 · 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 designSimulation or modeling
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
Published2020
Admission routes1
Has abstractyes

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