Shrinkage Mitigation of an Ultra-High Performance Concrete Submitted to Various Mixing and Curing Conditions
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Bibliographic record
Abstract
Ultra-High Performance Concretes (UHPC) are cement-based materials with a very low water-to-binder ratio that present a very-high compressive strength, high tensile strength and ductility as well as excellent durability, making them very interesting for various civil engineering applications. However, one drawback of UHPC is their pretty high autogenous shrinkage stemming from their very low water-to-binder ratio. There are several options to reduce UHPC shrinkage, such as the use of fibers (steel fibers, polypropylene fibers, wollastonite microfibers), shrinkage-reducing admixtures (SRA), expansive admixtures (EA), saturated lightweight aggregates (SLWA) and superabsorbent polymers (SAP). Other factors related to curing conditions, such as humidity and temperature, also affect the shrinkage of UHPC. The aim of this paper is to investigate the impact of various SRA, different mixing and curing conditions (low to moderate mixing temperatures, moderate to high relative humidity and water immersion) as well as different curing starting times and durations on the shrinkage of UHPC. The major importance of the initial mixing and curing conditions has been clearly demonstrated. It was shown that the shrinkage of the UHPC was reduced by more than 20% at early-age and long-term when the fresh UHPC temperature was closer to 20 °C. In addition, curing by water immersion led to drastic reductions in shrinkage of up to 65% and 30% at early-age and long-term, respectively, in comparison to a 20% reduction for fog curing at early-age. Finally, utilization of a liquid polyol-based SRA allowed for reductions of 69% and 63% of early-age and long-term shrinkages, respectively, while a powder polyol-based SRA provided a decrease of 47% and 35%, respectively.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it