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Record W4389376858 · doi:10.1139/cgj-2023-0072

Face failure of EPB shield tunnels in dry dense sand: a model test and DEM study

2023· article· en· W4389376858 on OpenAlexvenueno aff
Jun Wang, Guojin Lin, Ning Tian, Kun Feng, Guowen Xu, Xiongyu Hu, Ziquan Chen, Chuan He

Bibliographic record

VenueCanadian Geotechnical Journal · 2023
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsnot available
Fundersnot available
KeywordsGeotechnical engineeringShieldGeologyMining engineeringFace (sociological concept)Test (biology)Petrology

Abstract

fetched live from OpenAlex

This study aims at addressing the face failure of earth pressure balance (EPB) shield tunnels in dry dense sand by model tests and discrete element method (DEM) models. The model tests incorporated a miniature EPB shield which could fully reproduce the real tunnel construction of excavation and support. DEM models simulating the model tests were developed to capture the underlying face failure mechanism. Results show that both the limit support pressure obtained at chamber board and tunnel face increase with increasing C/ D ( C is tunnel cover depth, and D is tunnel diameter). The ratio of the former to the latter approximates 0.60 due to the soil retaining of cutter head panel, and it is independent of C/ D. The local face failure initializes around tunnel face and develops directly to the global failure outcropping the ground surface in one phase with C/ D ≤ 1.0, while the local failure develops to the global failure in three phases with C/ D = 2.0 due to the soil arching evolution. The soil arching gets weaker when it propagates upward, and the horizontal stress concentration in the longitudinal direction is stronger than the transverse direction due to the difference of arch foot.

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 score0.023
Threshold uncertainty score0.794

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.010
GPT teacher head0.208
Teacher spread0.198 · 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

Citations8
Published2023
Admission routes1
Has abstractyes

Explore more

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