Predicting tunnel-boring machine penetration rate utilizing geomechanical properties
Bibliographic record
Abstract
Predicting the penetration rate plays a key role in tunnel projects using a tunnel-boring machine (TBM). Developing accurate prediction models can improve project management, and save budget and time in tunnel projects. In this research, the Gelas water-tunnel project data were used to obtain new statistical models for predicting the TBM penetration rate per revolution (PRev) utilizing the toughness index ( T i ), modulus ratio ( E /UCS) and joint parameters ( J P ). The relationships between various geomechanical properties and rock classification systems, including uniaxial compressive strength, Brazilian tensile strength, Young's modulus, joint parameter, toughness index, rock quality designation, rock mass rating, geological strength index, rock mass quality and rock mass index, were analysed and considered on the TBM performance in sedimentary, igneous and metamorphic rocks. The statistical analysis clearly showed that T i revealed a significant correlation with the actual PRev ( R 2 = 0.75). In addition, the PRev was computed using T i , and J P showed good agreement with the coefficient of determination ( R 2 ): i.e. 0.79. The results indicated that the T i decreased by increasing the modulus ratio, so the PRev increased. This model can be used easily as it provides a straightforward predictive model using a multi-parameter model.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".