Clogging potential of tunnel boring machine (TBM): a review
Bibliographic record
Abstract
Tunnel boring machines excavating through soft soils face different challenges, one of which occurs when the soil sticks to the cutter face or the conveyor band and obstructs the machine. This phenomenon, commonly referred to as clogging, leads to wearing of the cutting wheel and transportation system, delays in the time schedule and economic loss. Although several laboratory devices can evaluate the adhesion mechanism of soil to metal, the method to measure adhesion has not been standardised. As clogging is also directly related to the construction phase, engineers are more concerned with methods to avoid this problem during construction. In this regard, the application of soil conditioners has become useful, the benefits of which include torque reduction, easier soil manageability and clogging reduction. However, the effectiveness of the soil conditioners is difficult to evaluate. To provide some insight into the topic, this paper describes the basic mechanism of clogging, the key parameters for its evaluation, the laboratory tests conducted up to date, the classification diagrams developed to assess clogging risk and mitigation of this risk in underground tunnelling. This paper also describes some of the additives and their functions, the ratios used for measurement and application, and the current tests to evaluate their performance. Finally, the conclusions summarise the current findings in the issue of clogging, pointing to the advantages and shortcomings of previous research, as well as some lines of investigation to improve identification and mitigation of this problem.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".