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Record W2727183045 · doi:10.1061/9780784480809.024

Construction and Verification Testing of Cement Deep Soil Mix Panels to Mitigate Seismic Displacements for the Evergreen Line Rapid Transit Project

2017· article· en· W2727183045 on OpenAlexaff
Ali Azizian, Brian E. Hall, Juan I. Baez, Alan Moat, Blair Squire

Bibliographic record

VenueGrouting 2017 · 2017
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsTetra Tech (Canada)SNC-Lavalin (Canada)
Fundersnot available
KeywordsGeotechnical engineeringEnvironmental scienceSoil waterGeologyEngineeringMining engineeringSoil science

Abstract

fetched live from OpenAlex

A 2 km section of the Evergreen Line Rapid Transit Project is supported on improved ground consisting of cement deep soil mix (CDSM) panels because of highly variable and challenging soil conditions, especially the potential for seismic liquefaction-induced lateral spreading. This paper discusses the selection of soil-cement parameters for use in the performance-based design, the preconstruction testing undertaken, and some construction challenges including the proximity to mainline railway tracks, highly variable soil types including sticky clays, the presence of obstructions (buried tree trunks, boulders, and glacial erratics) and artesian pressures. Wet grab samples were collected daily from select columns and field monitoring was conducted to demonstrate that the installation of CDSM panels would not adversely impact the safety of the adjacent tracks or other sensitive infrastructure. Significant challenges were experienced with verification testing because of the variable nature of the soils, difficult workability of sticky clay, core barrel size, and the abundance of gravel which effected the recovery of intact core samples.

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 score0.445
Threshold uncertainty score0.463

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 head0.034
GPT teacher head0.256
Teacher spread0.222 · 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

Citations2
Published2017
Admission routes1
Has abstractyes

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