A transformer-based machine learning model for optimizing the design of cementitious mixtures with mine tailings as supplementary cementitious materials
Bibliographic record
Abstract
Realizing the full potential of incorporating mine tailings as supplementary cementitious materials (SCMs) to replace ordinary Portland cement (OPC) requires carefully balancing the benefits—such as cost reduction and emissions mitigation—while ensuring the mixtures achieve the required strength. Given the demonstrated effectiveness of combining machine learning (ML) with optimization algorithms in similar multi-objective optimization (MOO) problems, for the first time, this study employed a novel tabular prior data fitted network (TabPFN) model to forecast the uniaxial compressive strength (UCS) of those mix designs. The TabPFN model outperformed traditional boosting ML models, achieving an R 2 of 0.973 and a low prediction error of 2.115 MPa. Notably, its pre-trained architecture reduced computational time by 1045 s. Building on this, a MOO case study was developed using the TabPFN model to predict UCS as the first objective, alongside separate equations used as objective functions to calculate cost and total emissions. This MOO problem was tackled using the non-dominated sorting genetic algorithm-II (NSGA-II). The optimized mixture designs achieved better balances between strength, cost, and emissions than those obtained through experimental methods, validating the use of this ML-based method for mixture design. Finally, a software tool—GreenMix AI—was developed to provide integrated access to the entire framework, translating advanced research into practical application. In essence, this research supports the reuse of mine tailings as SCMs and provides a practical pathway to developing more economical and sustainable cementitious mixtures.
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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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".