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Record W2036153492 · doi:10.1139/l06-052

Modeling the effect of corrosion on bond strength at the steel–concrete interface with finite-element analysis

2006· article· en· W2036153492 on OpenAlexfundvenueno aff
Lamya Amleh, Alaka Ghosh

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2006
Typearticle
Languageen
FieldEngineering
TopicConcrete Corrosion and Durability
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRebarMaterials scienceCorrosionBond strengthFinite element methodStructural engineeringSteel barCrackingComposite materialSlip (aerodynamics)MetallurgyEngineering

Abstract

fetched live from OpenAlex

This paper examines the basic influence of corrosion on bond strength at the steel–concrete interface and the associated slip and cracking. A nonlinear finite-element model is developed to account for the effect of corrosion on deterioration of the bond. Deterioration of the mechanical interaction between the corroding reinforcing steel and the concrete is modeled with the nonlinear finite-element program ABAQUS. The contact pressure normal to the steel–concrete interface is reduced when the concrete cracks, which occurs along with a decrease in the cross-sectional area of the steel bar and the decrease of the friction coefficient between the steel and the concrete. The loss of contact pressure and the decrease in the friction coefficient with the mass loss of steel bars are evaluated using pullout test specimens with different levels of rebar corrosion. Finally, the relationship between the loss of bond strength and the mass loss of the steel rebar is established. The model gives reasonably accurate predictions of bond strength for three independent experimental studies. Key words: bond, concrete, corrosion, mechanical interaction, reinforcing steels, slip, steel-concrete interface.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.079
Threshold uncertainty score0.937

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 head0.005
GPT teacher head0.181
Teacher spread0.176 · 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

Citations56
Published2006
Admission routes2
Has abstractyes

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