Artificial neural networks and support vector machines for more accurate cost estimation in underground mining: A contractor's viewpoint
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Accurate cost estimation is crucial in effective decision-making and evaluation in underground mining projects. Machine learning techniques have shown enormous potential in enhancing cost estimation accuracy in various industries. This study harnesses artificial neural networks (ANN) and Support Vector Machines (SVM) to estimate operating costs in underground mining. Special emphasis is placed on cost estimation from a contractor’s perspective. Mining contractors are sensitive to deviations from the estimated costs because slight deviations may result in losing a contract bid or financial loss in an awarded project. The proposed approach can help contractors make more informed decisions and improve project management. Comprehensive data containing various parameters that impact the cost of underground mining projects, such as equipment type utilization, rock type, and cross-sectional area, were collected. This dataset was used to train and evaluate ANN and SVM models that provide more accurate cost estimation for underground mining projects. The best model achieved a mean average percentage error (MAPE) of 5.31 % for the ANN model and 3.05 % for the SVM model, outperforming traditional cost estimation methods. This study demonstrates the potential of machine learning in enhancing the performance of the cost estimation process.
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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 it