A comprehensive study on physico-mechanical properties of non-metallic fibre reinforced SCC blended with metakaolin and alcofine
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
Abstract This study presents a detailed experimental investigation on the effects incorporating non-metallic fibers in hybrid form in self-compacting concrete (SCC). In this regard SCC was prepared with Alccofine and Metakaolin as partial replacement for cement in 15% and 20% respectively along with the hybrid fibre combinations namely abaca fibres (0.25%, 0.5% & 0.75%), polypropylene fibres (0.5%, 1%, 1.5% & 2%) and glass fibres (0.5%, 1%, 1.5%, & 2%). The fresh properties of SCC with and without hybrid fibre combinations were assessed through the standard tests such as slump flow, J ring and V-funnel tests. The conventional mechanical tests such as compressive strength test, split tensile strength test and flexural strength test were performed at 7 and 28 days. The experimental results reveal that the fresh properties of SCC were highly influenced by alccofine and Metakaolin adopted in this research. Furthermore, that the hybrid combination of abaca with polypropylene and glass fibres improved the mechanical properties of SCC and in particular the mix with 1% glass fibre and 0.25% Abaca fibre had shown better flexural and tensile strength behaviour. Microstructure analyses were also done to confirm the improvement in mechanical properties. The Scanning Electron Microscope images of the mix with 1% glass fibre and 0.25% abaca fibre showed less voids presence and presence of more hydrated components conveying that the usage of hybrid fibres had restricted the propagation of cracks there by reducing the percentage of voids and the use of metakaolin and alcofine helping in forming hydrated components at earlier stage leading to better strength.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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 it