Prediction and mathematical modeling of compressive strength of recycled concrete under different loading conditions by steel fibers
Bibliographic record
Abstract
Under the rapid development of China’s infrastructure, concrete materials are widely used. Traditional concrete materials have defects such as poor compressive performance, and steel fibre concrete has a broad engineering application prospect. In this study, the compressive performance of steel fibre recycled concrete was analysed and tested using experiments such as cubic compression test and split tensile compression test. Subsequently, the compressive strength prediction model of steel fibre recycled concrete is constructed by combining the experimental test results and the uniaxial compression constitutive model, and the prediction effect of the model is analysed. The results show that when the volume rate of steel fibre admixture in recycled concrete is 1.2%, the compressive strength of recycled concrete is the highest under different loading conditions, indicating that the admixture of steel fibre can improve the compressive performance of recycled concrete. It was also found that the prediction error of the prediction model for the compressive strength of concrete under standard curing conditions and low-pressure conditions averaged 1.49% and 1.19%, which has good prediction effect. The compressive strength prediction model proposed in this paper can achieve reliable prediction of the compressive properties of steel fibre recycled concrete, which lays a foundation for the reasonable use of recycled concrete materials under different conditions in infrastructure projects.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 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".