Developing interpretable machine learning models for predicting the compressive strength of roller-compacted concrete
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
Roller compacted concrete (RCC) mixed with industrial by-products has significant applications in pavement engineering. However, laboratory determination of its compressive strength (CS) has several time and resource constraints. Thus, this study employed four machine learning (ML) algorithms including Gradient Boosting Regressor (GBR) and Multi Expression Programming (MEP) etc. to develop predictive models for RCC's CS. The algorithm results revealed that GBR provided the highest accuracy achieving a testing R² value of 0.99. In contrast, MEP generated an empirical equation for predictions which other algorithms could not produce. Additionally, interpretable ML approaches such as shapely additive (SHAP) and individual conditional expectation (ICE) analysis were used to determine the most crucial inputs for CS determination. Furthermore, a graphical user interface (GUI) was developed to allow users to obtain rapid CS predictions based on RCC mixture composition thus facilitating informed decision-making in project design and execution.
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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 | 0.000 | 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 it