UHPFRC Permeability to Chloride under Service Load
Bibliographic record
Abstract
The effect of multiple microcracks on the chloride diffusion coefficient is critical to guarantee the design lifetime of Ultra-High Performance Reinforced Concrete (UHPFRC) under service conditions. Static four-points bending tests (4PBT) were carried out on UHPFRC beams to characterize its mechanical properties and cracking evolution and the tested beams were made of UHPFRCs containing 2% volume of stainless-steel fibers. A special test set-up was designed to apply and maintain a sustained bending moment on the UHPFRC beams. The service load was represented by spread microcracks in UHPFRC beams before reaching the maximum load. All cracks were monitored by Digital Image Correlation (DIC) during the loading process. Chloride ion permeability evaluation was measured with a sustained load or without load by a modified procedure of the accelerated migration test, after which chloride profiles were obtained from grinding tests. A 2D Finite-Element-Method model considering both moisture transport, multi-ionic diffusion, coupling effects of temperature, electrical field, and ionic force in its governing equations was adopted to simulate the process. All cracks were integrated in the mesh with adjusted diffusion coefficients according to crack widths across the mesh. The results contained different chloride penetration rates and chloride profiles in tested UHPFRC beams and simulation graphs under different loading levels. The influence of the microcrack width was clearly identified on the apparent chloride diffusion coefficient of the considered UHPFRC. All these results are precious information for a better prediction of the service lifetime of a UHPFRC structure.
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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 | 0.000 | 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".