Numerical Simulation of the Load Transfer Mechanism at UHPC–UHPC Interface
Bibliographic record
Abstract
Ultrahigh performance concrete (UHPC) has been used in a different range of applications, especially in bridge construction, due to its outstanding mechanical properties, ductility, and long-term durability. There has been a rapid increase in the use of precast UHPC systems. The weakest link in the UHPC precast system is the interface between the precast UHPC components. The interfacial bond performance between UHPCs cast at different times plays a key role to ensure a load transfer and to achieve a composite behaviour. It has been experimentally proven that the exposed fibers using pressure washing or grooved surface preparations are an effective method to treat the UHPC–UHPC interface, but the numerical simulation and appropriate modeling remain under-investigated. This study focuses on the numerical simulation of the interfacial bond strength using finite element modeling (FEM). The traction-separation relationship with parameters derived from an experimental program were used to calibrate and validate the FE model. The model used information obtained from specimens tested under tensile, shear, and a combination of compression-shear stresses. This paper discusses the modeling of the interface between UHPC cast at different times to accurately simulate the failure mode and load transfer at the interface.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".