Autogenous Shrinkage, Microstructure, and Strength of Ultra-High Performance Concrete Incorporating Carbon Nanofibers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The mix design of ultra-high performance concrete (UHPC) is complicated by the presence of many "ingredients." The fundamental packing density allows a simpler mix design with fewer ingredients to achieve optimum packing density and dense microstructure. The optimum particle grading increases the flowability of UHPC and eliminates entrapped air. This study presents a simplified particle grading design approach that positively influences the strength, autogenous shrinkage, and microstructure characteristics of UHPC. Carbon nanofibers (CNFs) of superior mechanical properties were added to enhance the strength of UHPC and to reduce its autogenous shrinkage. In addition, ground granulated blast-furnace slag (GGBS) was used as a cement replacement material to reduce the amount of cement in UHPC mixes. Test results showed that the presence of homogeneously dispersed CNF increased the compressive strength and compensated the autogenous shrinkage of UHPC. The findings indicated that an ideal particle distribution, which is close to the modified Andreasen and Andersen grading model, contributed to achieving high compressive strength and CNFs were capable of providing nano-bridges to compensate the shrinkage caused by GGBS.
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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