Prediction of the mechanical properties of cemented paste backfill using artificial intelligence approaches
Bibliographic record
Abstract
In the digital era, the mining industry benefits from powerful tools that can help to optimise underground backfilling operations and to increase overall safety. Indeed, with current progress in artificial intelligence (AI), machine learning (ML) creates state-of-the-art techniques in the mining sector that could significantly improve the productivity and efficiency of mining operations. The purpose of this study is to apply ML algorithms, including the gradient boosting regressor (GBR), the XGBoost regressor (XGBR), and the support vector regressor (SVR) to predict the uniaxial compressive strength (UCS) of cemented paste backfill (CPB). A total of 1,587 UCS data were used to train the ML algorithms, considering different variables such as the types of tailings, binder and their proportion, solid mass concentration, slump height, water quality, and curing time. The raw data were pre-processed before training the models, as well as their hyperparameters tuning was made by a random search method followed by 4-fold cross-validation. The prediction results show that the GBR algorithm is the most powerful one which has a coefficient of correlation (R) between predicted and experimental values equal to 0.99 and a root-mean-square error (RMSE) equal to 0.16. This prediction is validated through new-lab prepared CPB specimens.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".