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Record W3113867750 · doi:10.3390/civileng2010002

Analytical Investigation on the Effect of Test Setup on Bond Strength

2021· article· en· W3113867750 on OpenAlexaff
Konstantinos Tsiotsias, S. J. Pantazopoulou

Bibliographic record

VenueCivilEng · 2021
Typearticle
Languageen
FieldEngineering
TopicStructural Behavior of Reinforced Concrete
Canadian institutionsYork University
Fundersnot available
KeywordsBond strengthBondReinforcementStructural engineeringSlip (aerodynamics)Tension (geology)Bar (unit)Materials scienceCharacterization (materials science)MechanicsUltimate tensile strengthEngineeringComposite materialPhysicsAdhesiveNanotechnologyThermodynamics

Abstract

fetched live from OpenAlex

Experimental procedures used for the study of reinforcement to concrete bond have been hampered for a long time by inconsistencies and large differences in the obtained behavior, such as bond strength and mode of failure, depending on the specimen form and setup used in the test. Bond is controlled by the mechanics of the interface between reinforcement and concrete, and is sensitive to the influences of extraneous factors, several of which underlie, but are not accounted for, in conventional pullout test setups. To understand and illustrate the importance of specimen form and testing arrangement, a series of computational simulations are used in the present work on eight distinct variants of conventional bar pullout test setups that are used routinely in experimental literature for the characterization of bond-slip laws. The resulting bond strength increase generated by unaccounted confining stress fields that arise around the bar because of the boundary conditions of the test setup is used to classify the tests with respect to their relevance with the intended use of the results. Of the pullout setups examined, the direct tension pullout test produced the most conservative bond strength results, completely eliminating the contributions from eccentricity and passive confinement.

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)0.000
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 score0.277
Threshold uncertainty score0.358

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.014
GPT teacher head0.222
Teacher spread0.208 · 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

Citations9
Published2021
Admission routes1
Has abstractyes

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