Geotechnical Challenges Associated with the Design of the REM Project in Montreal
Bibliographic record
Abstract
In 2018, the Caisse de Dépôt et Placement du Québec (CDPQ) awarded a $6.3 billion design-build contract to the Joint Venture (JV) team NouvLR for the design and construction of a 67-km light rail system called the REM in Montreal, which will be one of the largest automated transportation systems in the world. The complexity of the project posed unique geotechnical challenges on many levels. Part of the alignment was constructed over peatland where low bearing capacity and excessive settlement obstacles needed to be overcome. Many small and large diameter utilities, running in the vicinity of proposed embankments, required the design of a protection system by mean of column supported embankment. Part of the alignment was to be constructed over an old landfill where the subsurface investigation indicated up to 9 m of solid waste. Presence of over 10 m of soft to firm silty clay required ground improvement using semi-rigid inclusions (such as Controlled Modulus Columns) to minimize settlement. Numerous segments along the project alignment were found to be underlain by potentially liquefiable soils where laboratory cyclic direct simple shear (DSS) tests were performed to assess liquefaction potential. Furthermore, over 25 km were constructed on 650-span elevated structure founded on single drilled shafts socketed into rock. To optimize the shaft design, three full-scale, bidirectional (Osterberg Cell) static load tests and two fully instrumented lateral load tests in critical areas were performed. The paper discusses the static load tests results as well as the different issues and concerns raised during the geotechnical design and how these were addressed.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".