Analyzing Self-Compacted Mortar Improved by Carbon Fiber Using Artificial Neural Networks
Bibliographic record
Abstract
This study presents an analytical framework, employing artificial neural networks (ANNs), to characterize the fresh properties of self-compacting mortar (SCM) reinforced with 6mm long carbon fibers at varying content ratios (0.05, 0.1, 0.2, and 0.4% by weight).Utilizing a multi-layered network approach with reactive error variance, the analysis delivers a nuanced understanding of the influence carbon fibers exert on SCM's fresh characteristics.It was observed that the inclusion of carbon fibers extended the passage time through a mini funnel and contracted the flow diameter, indicating alterations in workability.The ANN model demonstrated a high degree of predictive accuracy for fresh SCM properties, achieving a validity of 97.5% and a coefficient of determination (R 2 ) of 89.4%.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".