Artificial neural network based modeling of the coupled effect of sulphate and temperature on the strength of cemented paste backfill
Bibliographic record
Abstract
Among the different options for mine waste management, cemented paste backfills (CPB) have become important in mining operations around the world due to their environmental and economic benefits. The key design parameter of a CPB structure is its mechanical stability, which is commonly evaluated by the uniaxial compressive strength (UCS) of the CPB material. Experimental studies have shown that the sulphate present within the CPB and the curing temperatures can significantly affect the strength of CPBs. The increasing use of CPBs in underground mine operations as well as the subjection of CPBs to a large variability of thermal (curing temperature) and chemical (sulphate content) loads, make it necessary to model and quantify the coupled effect of sulphate and curing temperature on the strength (key design parameter) of CPBs. Therefore, the main objective of this study is to develop a methodological approach and a mathematical model based on an artificial neural network (ANN) to analyze and predict the effect of different amounts of sulphate on the strength of mature CPBs cured at various temperatures. Based on the experimental results of UCS tests from previous studies on various CPBs, the authors have developed an ANN model by using an ANN methodology implemented through MATLAB™. The developed model is validated with experimental data that is not used for the model development. The validation shows good agreement between the predicted and experimental data. The results from the ANN model of this study show that the coupled effect of curing temperature and sulphate significantly affects the strength of CPBs. This effect can be positive (strength increase) or negative (strength decrease) depending on the initial amount of sulphate content, the curing temperature, and type of binder. Furthermore, this study demonstrates that ANN can be used as a valuable tool to evaluate the coupled influence of sulphate and temperature on the strength of CPBs, i.e., it is a suitable tool for the optimization of a CPB mixture.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".