Advanced design of large cavern intersections in soft ground without conventional bar reinforcement
Bibliographic record
Abstract
Abstract The design of three mined stations in Toronto as part of the Eglinton Crosstown Light Rail Project involved several intersections of large tunnels situated in the heterogenous, water‐bearing glacial till deposits. The most challenging openings, 220 m2 each, were formed in the two sides of the 18 m high cross‐cut tunnels to allow the excavation of the 18 m wide platform caverns. A key objective for the design was the optimization of the tunnel shapes and the advanced simulation of the fibre reinforced shotcrete properties, to allow these openings in the initial tunnel linings to be achieved without conventional bar reinforcement or thickenings, thus simplifying and accelerating the construction, whilst reducing health and safety risks. Advanced numerical analyses were utilized to simulate the staged excavation and support steps introducing advanced material models that accounted for the non‐linear behaviour of both soil and structural elements. The end product was an optimized design of the tunnel linings with synthetic fibre reinforced shotcrete without conventional reinforcement that was successfully implemented in the construction.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".