Response to Movement Within an Open-Cut Rock Face: Case Study from Ottawa Light Rail Project, Ontario, Canada
Bibliographic record
Abstract
ABSTRACT: The Kiewit Eurovia Vinci (KEV) Partnership are currently designing and building the Ottawa Stage 2 LRT Project. The project includes a new 27 km LRT extension of the Confederation Line and includes 3.4 km of cut and cover tunnels and significant highway widening and structures scope. Brierley Associates supported KEV by providing temporary support of excavation system designs for various segments of the open-cut trench excavation through soils and rock. The rock along the project alignment consisted of layered limestone, dolostones and shale beds with predominantly subvertical and subhorizontal jointing. Based on the site investigation top of rock data, it was assumed that several faults would cross the alignment although they were not mapped. Given the rock mass conditions within the project area, a spot bolting rock support system was implemented for the vertical rock cuts in combination with regular rock face mapping and monitoring. Along one segment of the alignment, a large sub-vertical fault was exposed below the installed soldier pile and lagging shoring. During excavation, rock conditions and monitoring data indicated a potential rock slope instability in the vicinity of the fault along one wall of the excavation. The survey, engineering and contractor teams responses were coordinated to mitigate the risks to the project and personnel. Sequence of events, response successes and lessons learned are presented.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".