Using Tunnel Boring Machine Penetration Tests to Quantify Performance in Hard Rock
Bibliographic record
Abstract
Tunnel boring machine (TBM) penetration tests entail incrementally increasing TBM thrust from full stop to maximum speed and recording the penetration rate either at set thrust levels or at set times. TBM penetration test data can be analysed by plotting the penetration rate (distance/revolution) against the net cutter thrust (force per cutter) over the full range of thrust levels in the test, called the penetration-thrust graph. This research shows that the transition from excavation dominated by grinding to excavation dominated by chipping can be observed in penetration-thrust graphs. Correlating penetration test data to the geological and geomechanical characteristics of rock masses through which a penetration test is conducted provides the ability to reveal the efficiency of the chipping process in response to changing geological conditions. By analysing penetration test data from projects in the Swiss Alps, and published data from Singapore, this research shows that the strength of the rock is an important control on how much net cutter thrust is required to transition from grinding to chipping. It also shows that the geological characteristics of a rock will determine how efficiently chipping occurs once it has begun. In particular, geological characteristics that lead to efficient fracture propagation, such as fabric and mica content, will lead to efficient chipping. These findings will enable a better correlation between TBM performance and geological conditions for use in TBM design, as a basis for contractual payments where penetration rate dominates the excavation cycle and in further academic investigations into the TBM excavation process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".