Prediction of the stability of gob‐side entry formation by roof cutting by machine learning‐based models
Bibliographic record
Abstract
Abstract Gob‐side entry formation by roof cutting is a new technology for no pillar coal mining, which can maximize coal resources and reduce roadway drivage ratio. However, the mechanical behavior of the formed entry is complex while it is crucial to ensure the stability of the entry for mining safety. This paper proposed a machine learning‐based method for predicting the stability of the formed entry, which combined artificial neural network (ANN) with particle swarm optimization (PSO) algorithm or genetic optimization (GO) algorithm. The data set from 75 coal mining faces from 2009 to 2022 was employed to train and test the models. A descriptive variable of dynamic unstable distance was introduced to evaluate the stability state of the formed entry and six other parameters were chosen as influence parameters. The two intelligent models were compared with each other to have a comprehensive assessment. Model assessment indices such as R2, mean absolute error, mean absolute percentage error, and root mean square error were used to evaluate the accuracy of the models. The results of both developed models are promising, and the predictive accuracy of the PSO‐ANN model is higher than that of the GO‐ANN model. Through sensitivity analyses, it has been found that the coal seam thickness and roof rock hardness are the most important parameters for influencing entry stability. The developed method provides a practical tool for the prediction of entry stability and the optimization of entry design.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".