Numerical Analysis of Bond Strength in Pretensioned Concrete: Impact of Varying Tension Ratios on Seven-Wire Strand Using Tensioned Pull-Out Test
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".