Performance Prediction and Mix Ratio Optimization for Multielement Green High-Performance Fiber-Reinforced Cement Matrix Composite
Bibliographic record
Abstract
Green high-performance fiber-reinforced cement (GHPFRC) matrix composite is prepared by mixing matrix composites like slurry, mortar, or concrete with reinforcing materials like metal or inorganic nonmetal fiber, synthetic fiber, or natural organic fiber by a certain method. This composite is more energy-efficient, ductile, low-carbon, economic, and environmentally friendly than ordinary concrete. However, the performance of GHPFRC matrix composite has not been fully studied. The existing research only deals with the seismic performance and fire resistance of the material, failing to systematically discuss the optimal mix ratio. To solve the problem, this paper presents an optimization strategy for multielement GHPFRC matrix composite, and carries out multiple tests on its basic mechanical performance, toughness, impact resistance, shrinkage cracking, dry shrinkage performance, and durability. The test data on various indices verify the superior performance of the prepared multielement GHPFRC matrix composite. Further, the optimal mix ratio of the material was determined as: 60% cement, 30% fly ash, and 10% silica ash, with the water-cement ratio of 0.4, water reducer dosage of 1.5%, and quartz sand dosage of 500g.
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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 | 0.000 |
| 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 | 0.001 |
| 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 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".