Study on fatigue performance and rutting prediction model of steel slag asphalt mixture
Bibliographic record
Abstract
Steel slag, a significant industrial waste, poses environmental and land issues due to its accumulation. This research explores using steel slag to replace natural aggregates in steel slag asphalt mixture (SSAM) at varying levels by weight: 25%, 50%, 75%, and 100%. The research evaluated the pavement and fatigue performance of SSAM, noting that dynamic stability peaked at a 75% slag content. Although low-temperature flexibility decreased, it remained within acceptable limits. The mixture met or exceeded Chinese specifications for resistance to water and traffic stress. Fatigue life varied with slag content, decreasing at high levels due to increased stiffness and stress. A rutting prediction model for SSAM was developed, utilizing a back propagation neural network for accurate rut depth forecasts, demonstrating the model’s precision with a fit of 0.99744. This study suggests that optimized slag content enhances SSAM performance, offering a sustainable approach to managing steel slag.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".