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Record W4205867261 · doi:10.1177/14644207211067437

Dynamic analysis of soil-structure interaction shear model for beams on transversely isotropic viscoelastic soil

2022· article· en· W4205867261 on OpenAlexaff
Zele Li, Mohammad Noori, Dipanjan Basu, Ertuǧrul Taciroğlu, Zhishen Wu, Wael A. Altabey

Bibliographic record

VenueProceedings of the Institution of Mechanical Engineers Part L Journal of Materials Design and Applications · 2022
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsTransverse isotropyTimoshenko beam theoryFinite element methodBeam (structure)IsotropyViscoelasticityPartial differential equationMoving loadDifferential equationNewmark-beta methodMechanicsMathematicsShear (geology)Mathematical analysisPhysicsStructural engineeringMaterials scienceEngineeringOptics

Abstract

fetched live from OpenAlex

Dynamic response problem of the Timoshenko shear beam resting on the transversely isotropic viscoelastic foundation and subjected to a moving load is investigated. The extended Hamilton's principle is used to obtain the dynamic response equations of the beam-soil system. The corresponding partial differential equations are derived using variational principle. Using the analytical solutions, finite-element method, and central difference method, these differential equations are solved and mutually verified. The Newmark-β iterative algorithm is employed to decouple the dynamic equations of the beam-soil system. A modified two-parameter spring foundation model is used to simulate the dynamic characteristics of soil medium. Finite-element analysis demonstrates that the developed shear model of beam-soil is effective and accurate. Other numerical examples are carried out to analyze the effect of the shear beam, the load speed, and transverse isotropy of the dynamic medium model.

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: none
Teacher disagreement score0.690
Threshold uncertainty score0.427

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 head0.011
GPT teacher head0.217
Teacher spread0.206 · 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

Citations10
Published2022
Admission routes1
Has abstractyes

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