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Record W2885133455 · doi:10.1002/geot.201800009

EPB tunnelling through clay‐sand mixed soils: Proposed methodology for clogging evaluation

2018· article· en· W2885133455 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueGeomechanics and Tunnelling · 2018
Typearticle
Languageen
FieldEngineering
TopicTunneling and Rock Mechanics
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of CanadaQueen's UniversityDeutsche Forschungsgemeinschaft
KeywordsCloggingGeotechnical engineeringSoil waterTunnel boring machineEnvironmental remediationQuantum tunnellingMining engineeringEngineeringEnvironmental scienceSoil scienceMechanical engineeringMaterials scienceContamination

Abstract

fetched live from OpenAlex

Abstract The clogging of Tunnel Boring Machine (TBM) tools by soils has long been investigated, owing to the numerous difficulties arising in shield tunnelling as a result. Its occurrence leads to operation delays owing to the frequent and lengthy interventions required to unblock the soil stuck to the excavation tools and screw conveyor. Several authors have proposed laboratory tests for evaluating the clogging potential, however, those include limitations, such as not considering the clay fraction in a soil. One of these methods is the empirical stickiness evaluation, whereby a mixer and a beater are used to define a clogging evaluation parameter. Following an extended test campaign using soils with different clay contents and minerals, it became clear that this method was not adequate to provide reliable information regarding the tendency of a soil to clog in a tunnel drive. A new device was then implemented, which adds to the first method a kinetic energy impulse via dropping of the beater from a certain height. This combination of methods could provide a reasonable approximation of the potential for clogging to occur along Earth Pressure Balance Machine (EPB) tunnel drives. This paper presents the results of the proposed combined methodology for clogging evaluation, as well as the research evolution that led to the addition of the beater dropping stage.

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.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.809
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.083
GPT teacher head0.299
Teacher spread0.217 · 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