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Record W4401479235 · doi:10.56952/arma-2024-1236

Response to Movement Within an Open-Cut Rock Face: Case Study from Ottawa Light Rail Project, Ontario, Canada

2024· article· en· W4401479235 on OpenAlexaboutno aff
Anna M. Crockford

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsFace (sociological concept)Movement (music)Light railGeologyMining engineeringComputer scienceTransport engineeringEngineeringPublic transportSociology

Abstract

fetched live from OpenAlex

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.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.093
Threshold uncertainty score0.914

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 head0.012
GPT teacher head0.237
Teacher spread0.225 · 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
GenreEmpirical

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
Published2024
Admission routes1
Has abstractyes

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