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Record W4381704880 · doi:10.1201/9781003348030-195

Modernization of a century old Mont-Royal Tunnel

2023· book-chapter· en· W4381704880 on OpenAlexaffabout
M. Motallebi, B. Esmaeilkhanian

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldEngineering
TopicGeotechnical Engineering and Analysis
Canadian institutionsAecom (Canada)
Fundersnot available
KeywordsModernization theoryHistoryEngineeringPolitical scienceLaw

Abstract

fetched live from OpenAlex

Réseau Express Métropolitain (REM) is the new light rail transit network of Montreal, Canada. This electric and fully automated LRT network will facilitate mobility across the Greater Montreal Region by providing 67 km of twin tracks and 26 stations. This new LRT network uses existing Mont-Royal Tunnel (MRT) to connects Downtown Montreal to the north side of the Island of Montreal by passing through Mount Royal mountain. In addition, two of the project stations will be built inside this tunnel by enlarging it from a double-track tunnel to side-platform station at the location of these stations. The Mont-Royal Tunnel (MRT) is a century-old railway double track horseshoe tunnel. The tunnel is approximately 5 km long, 8.8 m wide and 5.5 m high, with a constant 0.6% grade. In order to use MRT in a modern transit system, the tunnel needed to be inspected, rehabilitated and be compliant to the recent safety standards. This paper presents a summary of history and characteristics of the existing tunnel plus the implemented steps to upgrade the tunnel. It includes the details of the structural inspection and rehabilitation methods of the tunnel. Further, this paper summarizes the procedures for the space proofing, construction of the separation wall, and the tunnel enlargement. It also describes parts of complications encountered during the constructions and solutions implemented to tackle these issues.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.115
Threshold uncertainty score0.859

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.162
Teacher spread0.154 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2023
Admission routes2
Has abstractyes

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