MWD data Analysis for Risk Assessment and Process Optimization in Tunneling
Bibliographic record
Abstract
In drill and blast operations, the collected Measure While Drilling (MWD) data can be used for analysis.In this study we process MWD data with machine learning (ML) methods to demonstrate how real-time risk assessment can be conducted: by predicting the volume of over-excavation, one can aim in reducing risk, lowering project costs, and minimizing environmental impact.The complexity of MWD data makes it necessary to address several challenges before utilizing the data within ML frameworks, namely the three steps (data pre-processing, feature extraction, and normalization) shall take place before further modelling.Here, we discuss the data preparation process, showing how the output from the correlation analysis impacts ML models accuracy.We will show that information from the raw MWD data is preserved and, even, enriched in the correlation analysis.Such information enrichment allows ML models to discover patterns implicitly related to changes in the rock mass conditions in MWD data.By including correlation analysis into the data preparation pipeline, combining it with encoding strategies, fine-tuning procedures, and careful selection of validation metrics, one can dramatically improve the accuracy of ML models in geotechnical applications.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".