Predicting workability and mechanical properties of bentonite plastic concrete using hybrid ensemble learning
Bibliographic record
Abstract
Heavy metal contamination in wastewater poses severe environmental challenges, highlighting the urgent need for efficient and cost-effective solutions. While bentonite incorporation in concrete mixtures has shown promise in adsorbing heavy metals, its experimental validation-through Bentonite Plastic Concrete (BPC)-is hindered by high costs, labor-intensive procedures, and the need for specialized equipment. This study overcomes these barriers by introducing hybrid ensemble learning models, optimized with Forensic-Based Investigation Optimization (FBIO), to predict BPC's workability and mechanical properties, including slump (S), tensile strength (TS), and elastic modulus (E). Using input parameters such as gravel, bentonite, silty clay, curing time, sand, cement, and water, models including Random Forest (RF), Adaptive Boosting (ADB), Extreme Gradient Boosting (XGB), and Gradient Boosting Regression Tree (GBRT) were developed. Notably, GBRT-FBIO achieved the highest accuracy for E predictions, while XGB-FBIO excelled for TS and S. Shapley Additive Explanation (SHAP) analysis identified water as the most critical factor influencing slump (+ 0.11) predictions while curing time emerged as the key determinant for TS (+ 0.18) and E (+ 0.12) predictions. Additionally, a user-friendly online tool was developed to enable the real-time application of these models, reducing reliance on costly experimental methods. This work addresses key challenges in experimental BPC testing, offering a transformative computational approach for advancing civil engineering materials research.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".