Traitement et analyse des paramètres de pilotage d'un tunnelier
Bibliographic record
Abstract
Tunnel boring parameters are used to control the good-functioning of the tunnel boring machine along the bored tunnel route and rarely to obtain more information about the nature or the mechanical behavior of the bored soil. Actually, they can bring very informative data, firstly, to improve the geological section to have the exact tunnel length in each soil formation and, secondly, to quantify the variability of the boring process along the tunnel route. Previously, a mechanical parameter is obtained from the combination of three tunnel boring parameters, which are thrust, penetration rate, and rotary speed. From statistical and geostatistical methods, the drillability signal, which can be seen as a time series, is divided into a set of stationary subdomains. The resulting series is then a stationary one, by zones, whose analysis can bring more information about the tunnel length for each soil formation and on the variability of the boring process. This last piece of information might then be utilized by contractors to explain some low advance rate totally unexpected before boring.Key words: tunnel boring machine, boring parameters, drillability, homogeneous zone, variability, statistic, geostatistic.
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 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 | 0.001 |
| 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 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".