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Record W2770837187 · doi:10.1515/corrrev-2017-0086

The performance of corroded lap splices in reinforced concrete beams

2017· article· en· W2770837187 on OpenAlexaff
S. J. Pantazopoulou, M. Petrou, Vasiliki Spastri, Nikos Archontas, Christos Christofides

Bibliographic record

VenueCorrosion Reviews · 2017
Typearticle
Languageen
FieldEngineering
TopicConcrete Corrosion and Durability
Canadian institutionsBerger (Canada)York University
Fundersnot available
KeywordsMaterials scienceCorrosionComposite materialCrackingTension (geology)Reinforced concreteStructural engineeringUltimate tensile strengthEngineering

Abstract

fetched live from OpenAlex

Abstract This article presents the results of an extensive experimental program containing 22 beams with tension lap splices in the central region. The beams were preconditioned under simulated corrosion up to specific levels of bar section steel loss and cover cracking in the lap region. They were subsequently tested under four-point loading so as to place the corroded lap splice zones in tension. To prevent corrosion outside the study region, the beams were wrapped with fiber-reinforced polymers outside the laps – this also served to protect them from premature shear failure as the objective was to study failure in the lap zone. The objective of the experiment was to assess the residual anchorage capacity of such zones. The parameters of the experimental study were the extent of corrosion and the available length of lap splicing of longitudinal tension reinforcement. Corroded bond strength was determined from the short-length lap splices, where it may be assumed that stresses are uniformly distributed over the lapped zone; longer specimens were considered in order to examine how the redundancy provided by the longer contact length may improve the resilience and deformation capacity of the corrosion-damaged component prior to bond failure.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.569
Threshold uncertainty score0.481

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 science0.0010.000
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 head0.023
GPT teacher head0.262
Teacher spread0.239 · 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

Citations4
Published2017
Admission routes1
Has abstractyes

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