Strength prediction of recycled aggregate concrete under sulfate attack using SVR–NSGA-II
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study investigates the prediction of residual compressive strength and mix proportion optimization of recycled aggregate concrete (RAC) under sulfate attack using machine learning algorithms. A database with 101 effective samples was used, considering 12 input parameters including raw materials, corrosive media, and exposure conditions and other relevant factors. A support vector regression model was developed to predict RAC strength, showing superior generalization and accuracy compared to traditional mathematical models. Additionally, a combination of nondominated sorting genetic algorithm II and ideal point method was employed to optimize RAC mix proportions, achieving both excellent sulfate resistance and cost efficiency. This research provides intelligent, efficient, and precise references for RAC application in engineering practice.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 it