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Record W2781944492 · doi:10.1080/1064119x.2017.1412549

Hydromechanical behavior of radially multilayered cylinders under time-varying loads

2018· article· en· W2781944492 on OpenAlexaff
Feng Zhou, Zhu Song, Cheng Lin

Bibliographic record

VenueMarine Georesources and Geotechnology · 2018
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsUniversity of Victoria
FundersNational Natural Science Foundation of China
KeywordsMaterials scienceGeotechnical engineeringStructural engineeringGeologyMechanicsEngineeringPhysics

Abstract

fetched live from OpenAlex

Geotechnical strata are often treated as horizontally homogeneous for hydromechanical analysis due to the vertical deposition of geological layers; however, such a treatment becomes no longer valid when vertical drilling or construction causes the localized disturbance of subsurface, which would result in radial heterogeneity of geomaterials. This paper presents a poroelastic solution for the saturated multilayered cylinder where multilayer is used to represent radial heterogeneity. After the application of Laplace transform, the governing equations in cylindrical coordinates are derived to obtain the stiffness matrix between stresses, displacements, and pore water pressure. The global matrix is assembled by the boundary conditions and the compatibility of interfaces between adjacent layers. Under time-dependent horizontal compression loads, a parametric study is performed for a cylinder comprised of two layers with distinct properties, and the results show that the load frequency and radial heterogeneity play a significant role in hydromechanical behavior of geomaterials: (1) the time-varying loading can induce a negative pore pressure, and the influence of cyclic loading with a high frequency is limited near the outer surface; (2) the radial heterogeneity due to permeability and compressibility affects the development of pore pressure.

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 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: Empirical
Teacher disagreement score0.546
Threshold uncertainty score1.000

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 integrity0.0000.000
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 head0.005
GPT teacher head0.188
Teacher spread0.184 · 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

Citations2
Published2018
Admission routes1
Has abstractyes

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