Predicting compressive strength of red mud-based geopolymers via machine learning: Insights from experimental validation
Bibliographic record
Abstract
Red mud, a challenging industrial by-product, poses a significant environmental challenge due to its increasing stockpiling. Its use as environmentally functional materials received increasing attention but requires good understanding of the compressive strength. This study employed machine learning (ML) approaches to develop a predictive model linking the composition (precursor, alkali activator) and curing conditions of red mud-based geopolymers (RMG) to their compressive strength. Among the nine studied ML algorithms, XGB model yielded the highest accuracy, achieving a coefficient of determination (R 2 ) above 0.9 in both training and testing. To improve the explainability of ML models, Shapley Additive exPlanations (SHAP) was employed to interpret the relative influence of input features on compressive strength prediction. The analysis highlighted the critical impact of calcium oxide content (RM_C), curing time (CC_CT), and alkali activator modulus (AC_S/N). Experiments were also designed based on these insights to validate the model's behavior concerning these key factors, with an average prediction accuracy error within 30%. Consequently, the machine learning based approach enhances the development efficiency for RMG, providing a new paradigm for the efficient materialization of similar solid wastes for sustainable development. • Machine learning models were developed to predict the compressive strength of RMG • XGB model achieved R 2 > 0.9 in both training and testing • SHAP analysis identified key strength-governing factors to guide experimental design • Experiments demonstrated the reliability of XGB model • Results can support the development of eco-friendly building materials from red mud
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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".