Tensile Creep of Metakaolin-Limestone Powder Ultra-High- Performance Concrete
Bibliographic record
Abstract
An investigation was performed on the drying shrinkage and tensile drying creep characteristics of a nonproprietary ultra-high-performance concrete (UHPC) mixture. The mixture was formulated using metakaolin as the supplementary cementitious material (SCM) and limestone powder as the mineral filler. Cylindrical specimens with dimensions of 52 x 400 mm (2.05 x 16 in.) were fabricated and loaded at 7 and 11 days from casting to various stress levels for 90 days. Additional specimens were fabricated from a proprietary mixture with a silica fume-ground quartz formulation to study the effects of mixture composition. Simultaneous free drying shrinkage measurements were recorded in accompanying specimens placed in the same room environment. Attention was given to the effect of the casting orientation, age at loading, and mixture composition on the drying shrinkage and drying creep behavior of the samples. These tests show that the metakaolin-limestone powder mixture has significantly lower drying shrinkage and specific drying creep than the silica fume-ground quartz mixture. Additionally, the age at loading influences primary creep behavior while not affecting secondary creep at the same stress level. It seems that fiber orientation plays a significant role in the drying creep behavior of UHPC and that cracked UHPC under constant tensile stress undergoes a significant amount of fiber slip.
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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 | 0.001 | 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) | 0.003 | 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".