The Influence of Various Factors on the Drying Shrinkage of Basalt Fibre Reinforced Cement-Based Composites Designed by the Taguchi Method
Bibliographic record
Abstract
Abstract Cement-based composites are widely used for various construction applications due to their outstanding mechanical and durability performance. However, the high drying shrinkage of cement-based composites has resulted in detrimental effects on its long-term performance. Drying shrinkage in cement-based composites causes cracks that serve as a pathway for the ingression of deleterious materials into the composites. Therefore, improving the drying shrinkage resistance of cement-based composites will result in an enhancement of the overall performance of the composites. The incorporation of short fibres and supplementary cementitious materials are some of the effective ways to improve the resistance of cement-based composites against drying shrinkage. Therefore, this study aims to evaluate the effect of the incorporation of basalt fibres and fly ash on the drying shrinkage of cement-based composites. This paper presents the results from the experimental and numerical investigation on the influence of various factors on the drying shrinkage of basalt fibre reinforced cement-based composites. The mixtures evaluated in this study were designed using the Taguchi method and a total of nine mixtures were made. The influence of the fly ash, water, sand and basalt content on the drying shrinkage of the mixtures were investigated. The findings from this study showed that the use of fly ash to cement ratio of 4, sand to binder ratio of 1, water to binder ratio of 0.25 and basalt fibre dosage of 2% is optimum to reduce drying shrinkage.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".