Key mechanical properties and microstructure of carbon fibre reinforced self-consolidating concrete
Bibliographic record
Abstract
This paper presents the key mechanical properties and microstructure of different carbon fibre reinforced self-consolidating concrete (CFRSCC) mixtures. Two different water-to-binder (W/B) ratios of 0.35 and 0.40 were used to produce ten CFRSCC mixtures including 0–1% carbon fibres by volume of concrete. The key mechanical properties such as compressive strength, splitting tensile strength, flexural strength or modulus of rapture, and toughness or fracture energy of CFRSCCs were determined. In addition, the load-deflection behaviour was studied for all CFRSCCs. The microstructure of all CFRSCCs was also observed via scanning electron micrographs (SEMs) of the fracture surface to examine the distribution and failure mode of carbon fibres in self-consolidating concrete. Test results revealed that the increased amount of carbon fibres decreased the compressive strength of CFRSCC by 36.6–58.9% depending on W/B ratio and curing age. However, the higher amount of carbon fibres increased the splitting tensile strength of CFRSCC by 13.1–17% at different W/B ratios and curing ages. Also, the flexural strength and toughness of CFRSCC was increased by 3.6% and 41.4%, respectively, for 0.25% carbon fibres. The load-deflection behaviour diagrams showed that the CFRSCC with 0.25% carbon fibres had the best post-peak response under loading. Furthermore, the SEMs exhibited that the CFRSCCs with 0.35 W/B ratio were denser with lesser pores than the CFRSCCs with 0.50 W/B ratio. Carbon fibres were well-distributed in concrete when the fibre content was 0.25%. It was also observed from SEMs that carbon fibres failed either by pullout or breakage under loading.
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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 | 0.000 |
| 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) | 0.001 | 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".