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Record W4403092576 · doi:10.11159/ijci.2024.018

Numerical Analysis of Bond Strength in Pretensioned Concrete: Impact of Varying Tension Ratios on Seven-Wire Strand Using Tensioned Pull-Out Test

2024· article· en· W4403092576 on OpenAlexvenueno aff
Zaher Alkurdi, Tamás Kovács

Bibliographic record

VenueInternational Journal of Civil Infrastructure · 2024
Typearticle
Languageen
FieldEngineering
TopicStructural Behavior of Reinforced Concrete
Canadian institutionsnot available
Fundersnot available
KeywordsTension (geology)Structural engineeringMaterials scienceComposite materialGeotechnical engineeringEngineeringUltimate tensile strength

Abstract

fetched live from OpenAlex

Bond strength in pretensioned members is a critical factor influencing the structural integrity and durability of concrete structures.This study explores the bond behavior in such members, focusing on the flexural bond, through detailed numerical modeling.Using a validated finite element (FE) model, both simple and tensioned pull-out tests were simulated to examine the differences in bond strength and the impact of varying tension ratios.The study employed seven-wire strands and lightweight aggregate concrete, with three-dimensional (3D) elements representing the concrete and reinforcement bars.The bonded interface was modeled using 3D isoparametric gap elements with a pressure-sensitive Mohr-Coulomb frictional interface.The results indicated that pretensioning below the yield strength threshold had no significant effect on bond strength compared to the bond strength observed in the simple pull-out test.However, a decrease in bond strength was observed when the pull-out test was conducted just prior to or during the yielding of the strand, with a more substantial reduction occurring when the test was initiated after yielding.Additionally, changes in strand diameter due to the Poisson effect were more pronounced under higher stress conditions, further influencing bond strength.

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 categoriesMeta-epidemiology (narrow)
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 score0.408
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.011
GPT teacher head0.282
Teacher spread0.271 · 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.

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

Citations0
Published2024
Admission routes1
Has abstractyes

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