TRAM: A Graphical User Interface for Risk Analysis in the Underground Coal Mines
Bibliographic record
Abstract
Over the last few decades, the Australian, New Zealand, Canada, UK, USA and South African mining industries have applied risk management techniques to regulate the hazards in mines. In the Indian mining industry, it was mandated only after the revision of the Coal Mines Regulations in 2017. An effective risk assessment is required to develop a practical risk management plan. TRAM (Tool for Risk Assessment in Mines) is a Graphical User Interface (GUI) that acts as a risk analysis and ranking tool for underground coal mines. This study aims to describe the structure and application of TRAM developed. TRAM is based on the proposed methodology which incorporates Fuzzy logic, the Analytic Hierarchy Process (AHP) and VIKOR, which stands for Multi-Criteria Optimization and Compromise Solution. In the proposed methodology, three risk parameters, consequence (C), exposure (E) and probability (P) were used to assess the risk of identified hazards, hazard groups and overall mine. A case study of risk analysis of an underground coal mine is used to illustrate the application of the TRAM. The developed TRAM has a user-friendly interface, allowing even inexperienced experts to perform effective risk analysis for mines in a short period of time.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.051 | 0.010 |
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".