The Role of Slurry Microtunnelling for Pipeline Installations in Hard Rock Conditions
Bibliographic record
Abstract
Abstract Pipe Jacking with Slurry Microtunnel Boring Machines (MTBMs) plays a pivotal role for challenging sections along the main Canadian pipeline routes, through the mountainous areas characterized by high rock strengths. In addition to the well-known pipeline installations with HDD and Direct Pipe, Slurry MTBMs for pipe jacking can be used for safe and precise excavation of small-diameter casing tunnels, also in challenging ground, where alternative methods reach their technical or economic limits. In small-diameter tunnelling through hard rock, the continuous further development of technologies plays a key role to overcome the limitations in rock strength and drive length and to improve performance. The cutting wheel’s tooling composition is one of the critical aspects in rock applications. Tool size and arrangement are determined based on the rock properties, to obtain reasonable rock chip sizes which can be handled by the discharge system. Three main cutting tool types can be considered for MTBMs: disc cutters, TCI cutters or milled tooth cutters. The most common cutting tool remains the disc cutter, which is well known from large diameter hard rock TBMs. Small-diameter hard rock Slurry MTBMs need to have a capability of high jacking forces and both a strong main bearing and cutter bearing. For the latest AVN 800 for hard rock, TCI cutters have been chosen. TCI cutters exert a point load force on the rock, resulting in numerous small chips. Due to the tungsten carbide insert, the TCI cutter is considered especially wear-resistant, which is beneficial for long drives in small diameters, where cutterhead interventions are not possible. In hard rock conditions with high rock strength and/or abrasivity, the applicability of Slurry MTBMs has been especially limited in the non-accessible diameter range in the past. With the latest AVN 800 for hard rock microtunnelling, Herrenknecht has developed a powerful machine concept, enabling drives of up to 200 meters without interventions. Longer drives make microtunnelling a more economic and more environmentally friendly construction method, as the number of shafts can be reduced. New opportunities for clients and consultants in the planning of tunnel routes for different pipeline installations can be considered, making trenchless technologies even more competitive and cost-effective, and improving public acceptance at the same time.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".