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Record W4254273232 · doi:10.32920/ryerson.14652741

Bond Degradation and Residual Flexural Capacity of Corroded RC Beams

2021· preprint· en· W4254273232 on OpenAlexaff
Hao Wu

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicConcrete Corrosion and Durability
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsFlexural strengthStructural engineeringMaterials scienceResidualBeam (structure)Bond strengthReinforced concreteComposite materialResidual strengthBondComputer scienceEngineeringLayer (electronics)Adhesive

Abstract

fetched live from OpenAlex

An analytical model is developed to predict the residual flexural capacity of corroded RC members. This was established by first developing an analytical model to calculate the residual bond strength at steel-concrete interface. The bond model is then implemented within the framework of the moment resistance method, and a new strain compatibility analysis was developed: to account the analysis of a corroded reinforced concrete beam, to incorporate dependence of the bond response on the stress strain and damage state of the concrete and steel. Method for calculating flexural capacity of corroded RC beams is then proposed, which is based on flexural analysis of RC beams that considers the effect of bond degradation. The predicted results of these models correlated very well with results observed in various experimental studies. This indicates that those developed analytical models tend to estimate conservatively the residual bond strength and flexural capacity of corroded RC beams. .

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.028
GPT teacher head0.228
Teacher spread0.200 · 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 source (direct Gemma or distilled Codex), 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
Published2021
Admission routes1
Has abstractyes

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