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Modeling of Vehicle-Track-Tunnel-Soil System Considering the Dynamic Interaction between Twin Tunnels in a Poroelastic Half-Space

2019· article· en· W2984654383 on OpenAlexaff
Shunhua Zhou, Chao He, Peijun Guo, Honggui Di, Xiaohui Zhang

Bibliographic record

VenueInternational Journal of Geomechanics · 2019
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPoromechanicsTrack (disk drive)GeologySpace (punctuation)Geotechnical engineeringEngineeringAerospace engineeringStructural engineeringComputer scienceMechanical engineering

Abstract

fetched live from OpenAlex

The vibration prediction of a vehicle-track-tunnel-soil dynamic system is quite meaningful in engineering practice. However, the complexity involved in modeling the underground environment makes most previous studies ignore the impact of a neighboring tunnel. In this paper, a three-dimensional analytical model is presented to predict the vibrations of the vehicle-track-tunnel-soil system in a poroelastic half-space, with the dynamic interaction of two neighboring tunnels being considered. The vehicle was considered as a multibody system, and the slab and rail were simulated as Euler–Bernoulli beams. The twin tunnels were simplified as cylindrical thin shells, and the soil surrounding the tunnel was simulated as a saturated porous medium. Wave translation and transformation were applied to satisfy the boundary conditions of the interfaces between the tunnels and soil as well as the ground surface. By coupling the twin-tunnel model with the vehicle-track model, an analytical model for the vehicle-track-tunnel-soil system was obtained. The proposed model was validated by means of comparison with the existing solution. The numerical results demonstrate that the neighboring tunnel and water saturation have a significant effect on soil vibrations.

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 score0.203
Threshold uncertainty score0.516

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 integrity0.0000.001
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 head0.008
GPT teacher head0.222
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

Citations22
Published2019
Admission routes1
Has abstractyes

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