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Record W4415486613 · doi:10.1139/cgj-2025-0444

Tunnel face instability in composite strata during shield tunneling: a case study

2025· article· en· W4415486613 on OpenAlexvenueno aff
Sen Teng, Jian Shi, Mingkai Zhao

Bibliographic record

VenueCanadian Geotechnical Journal · 2025
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Analysis
Canadian institutionsnot available
FundersNatural Science Foundation of Shandong Province
KeywordsShieldInstabilityComposite numberArchParametric statisticsQuantum tunnellingFace (sociological concept)Failure mechanism

Abstract

fetched live from OpenAlex

Accurately predicting tunnel face stability during shield tunneling in composite strata remains a significant challenge due to the pronounced heterogeneity and contrasting mechanical behaviors of stratified geological formations. To address this issue, this study establishes a refined coupled FDM–DEM numerical model of a tunnel boring machine (TBM), capable of realistically capturing the excavation-induced disturbance process. A parametric study was conducted to evaluate the effects of the composite ratio and the chamber fill ratio on tunnel face behavior, including failure zone development, TBM thrust, and torque evolution. The results indicate that tunnel face instability evolves through a typical three-stage process: initial disturbance, failure development, and failure propagation with arch reformation. The extent of failure is positively correlated with the composite ratio and negatively correlated with the chamber fill ratio. A higher composite ratio amplifies disturbance effects due to increased exposure to weaker strata, while greater chamber fill enhances face support and suppresses failure propagation. These findings enhance the understanding of tunnel face failure mechanisms in composite strata and support the rational design of shield tunneling parameters under complex geological conditions.

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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.002
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 head0.223
Teacher spread0.213 · 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.

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
Published2025
Admission routes1
Has abstractyes

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