Research on optimization model of concrete proportioning based on particle swarm algorithm in construction engineering technology
Why this work is in the frame
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Bibliographic record
Abstract
Particle swarm algorithm, as a kind of population intelligent optimization algorithm, shows great potential in solving multivariate and nonlinear optimization problems due to its simple and efficient characteristics.The article constructs a concrete ratio optimization model in construction engineering technology, which is supported by particle swarm algorithm as the main technology.The model also integrates the least squares support vector regression algorithm, which makes it not only simple ratio optimization, but also has the function of concrete performance prediction.The relative error of the model in predicting the physical properties of concrete is small, less than 5%, which improves the reliability of concrete proportioning.The concrete samples generated by the model with five different ratios have better physical properties for daily needs.In the durability test, the concrete sample with proportion 4 showed the best performance in terms of mass loss rate and impermeability, which were 3.52% (after 400 cycles) and 156.44C (after 56d), respectively.And all the concrete samples used were in the range of proportional qualification and the cost was 5.99% to 28.61% lower than the comparison method.
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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 | 0.001 | 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 | 0.001 |
| 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