Predicting High-Performance Concrete Compressive Strength Using Features Constructed by Kaizen Programming
Bibliographic record
Abstract
The compressive strength of high-performance concrete (HPC) can be predicted by a nonlinear function of the proportions of its components. However, HPC is a complex material, and finding that nonlinear function is not trivial. Many distinct techniques such as traditional statistical regression methods and machine learning methods have been used to solve this task, reaching considerably different levels of accuracy. In this paper, we employ the recently proposed Kaizen Programming coupled with classical Ordinary Least Squares (OLS) regression to find high-quality nonlinear combinations of the original features, resulting in new sets of features. Those new features are then tested with various regression techniques to perform prediction. Experimental results show that the features constructed by our technique provide significantly better results than the original ones. Moreover, when compared to similar evolutionary approaches, Kaizen Programming builds only a small fraction of the number of prediction models, but reaches similar or better results.
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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) | 0.000 | 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".