Effect of Abaca Fiber and Basalt Fiber in Mono and Hybrid Incorporating in Improving the Mechanical Properties of Self‐Compacting Concrete
Bibliographic record
Abstract
To address the growing demand for environmentally friendly building materials, increasing attention is being given to adding natural fibers to concrete. While incorporating natural fibers enhances stability by reducing flow, their use in self‐compacting concrete (SCC) presents challenges in maintaining flowability. This study investigates the impact of mono and hybrid natural fibers on the fresh and mechanical properties of SCC, aiming to create more sustainable and cost‐effective concrete by identifying optimal dosages without using mineral admixtures. Abaca fiber (AF) of 50 mm length at a dosage of 0.25% and 0.5% and basalt fiber (BF) of 12 mm length were incorporated in SCC from 0.25% till 2% at 0.25% increment, and the optimum level of usage was identified based on the fresh property tests like slump flow diameter, T500 test, and mechanical tests like compressive strength and split tensile strength after 7 and 28 days of normal water curing. It was observed that AF of 0.25% was considered the optimal dosage, as its compressive strength and tensile strength at 28 days was 4.25% and 8.03%; 11.42% and 6.84% greater than conventional concrete, and AF of 0.5% mix. BF with 0.25%, 0.75%, 1.25%, and 1.75% provided good strength in all parameters, and 1.25% was its optimum dosage. In hybrid fiber mixes, the optimal dosages from mono fiber mixes were combined, and their mechanical behavior like compressive strength, split tensile strength, impact strength, and flexural strength was tested, and the selected specimens were analyzed for microstructural changes using scanning electron microscopy (SEM) to validate the results. The finding indicated that 0.25% AF and 0.25% BF mix achieved better flowability and the highest compressive strength compared to other combinations. In addition, the mix of 0.25% AF and 1.75% BF demonstrated tensile, impact, and flexural strength improvements of 21.42%, 8.21%, and 94.19%, respectively, over control concrete. It is concluded that AF‐based SCC (A‐SCC) and BF‐based SCC (B‐SCC) could not comply with EFNARC norms requiring increased superplasticizer for flow, but the strength parameter was higher in FR‐SCC than the conventional SCC.
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.
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".