Assessment of compressive strength of eco-concrete reinforced using machine learning tools
Bibliographic record
Abstract
Predicting the compressive strength of Compressed Earth Blocks (CEB) is a challenging task due to the nonlinear relationships among their diverse components, including cement, clay, sand, silt, and fibers. This study employed PyCaret, an automated machine learning platform, to address this complexity by developing and evaluating predictive models. The analysis demonstrated that fiber content exhibited a strong positive correlation with cement content, with a correlation coefficient of 0.9444, indicating a significant influence on compressive strength. Multiple machine learning algorithms were tested using metrics such as the coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE) to assess model performance. Among these, the Extra Trees Regressor showed the best predictive capability with R2 = 0.9444 (highly accurate predictions), RMSE = 0.4909 (low variability in prediction errors) and MAE = 0.1899 (minimal average prediction error). The results confirm that PyCaret effectively automates the machine learning workflow, enabling accurate modeling of complex material behavior. The Extra Trees Regressor outperformed other algorithms due to its ability to handle highly nonlinear and multivariate datasets, making it particularly well-suited for predicting the compressive strength of CEB. This approach offers a significant advantage over traditional laboratory testing, which is time-consuming and resource-intensive. By incorporating machine learning techniques, especially using PyCaret’s streamlined processes, the prediction of CEB strength becomes more efficient and reliable, providing a practical tool for engineers and researchers in material science.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".