DESIGN AND CONTRACTING STRATEGY FOR A SHORT TUNNEL IN MIXED GROUND CONDITIONS
Bibliographic record
Abstract
One of the key decisions in the planning and implementation of a tunnel project is the selection of the most appropriate tunnelling technology. Decision factoring tunnel projects often include: subsurface conditions; groundwater; tunnel size and spacing; depth below grade; proximity to buildings and facilities; local contractor capability and availability; risks due to ground loss; schedule; and, of course, costs. Approximately 40% of the City of Edmonton Light Rail Transit system's 12.3 km consists of underground works successfully constructed using Tunnel Boring Machine (TBM), Sequential Excavation Method (SEM) tunnelling and cut-and-cover techniques. The latest LRT extension consists of twin tunnels of complex geometry only 300 meters long, which pass under existing buildings and utilities and through a number of geological units. Due to this relatively short length of tunnel, the difficult ground conditions and the lack of local tunnel construction experience, the decision regarding the most appropriate tunnelling technology was perhaps more challenging than for any of the City's previous tunnel projects. This paper presents the design and contracting strategies implemented to mitigate tunnelling risks and to maximize interest and competition for the project.
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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 | 0.003 | 0.002 |
| 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) | 0.006 | 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".