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Record W2607243881 · doi:10.1139/cgj-2015-0389

Effects of twin-tunnel excavation on an existing horseshoe-shaped tunnel considering the influence of a settlement joint

2017· article· en· W2607243881 on OpenAlexvenueno aff
Biao Jiang, Li’ang Chen, Jingsi Yang, Shuying Wang, Charles Wang Wai Ng

Bibliographic record

VenueCanadian Geotechnical Journal · 2017
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsnot available
Fundersnot available
KeywordsGeotechnical engineeringCentrifugeSettlement (finance)Joint (building)ExcavationBending momentTunnel constructionGeologyTransverse planeEngineeringStructural engineering

Abstract

fetched live from OpenAlex

In urban centres, the construction of a new tunnel would cause ground disturbance and affect any adjacent existing tunnel. Tunnel–tunnel interaction problems have not yet been comprehensively studied. In particular, the response of an existing horseshoe-shaped tunnel to the construction of two perpendicularly undercrossing tunnels remains unclear. In this study, three-dimensional centrifuge tests and three-dimensional numerical analysis were designed and conducted in dry sand to investigate the effects of twin-tunnel undercrossing on an existing horseshoe-shaped tunnel considering the influence of a settlement joint. It is found that the adverse effects caused by the tunnel excavated later were smaller than those by the earlier one. For the existing tunnel without a settlement joint, the settlement at the invert and crown along the longitudinal direction showed sagging and hogging curves, respectively, owing to significant vertical elongation of the middle section. The presence of a settlement joint resulted in an increase in the settlement of the ground surface and the existing tunnel by over 100%. In both cases, larger bending strains were induced in the transverse direction than in the longitudinal direction, particularly around the corners.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.579
Threshold uncertainty score0.696

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0010.000
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 head0.018
GPT teacher head0.232
Teacher spread0.214 · 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

Citations32
Published2017
Admission routes1
Has abstractyes

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