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Record W4415436639 · doi:10.1134/s0025654425603295

Analytical Study of Torsional Wave Behavior in Graded Poroelastic Layer Bonded to Viscoelastic Foundation

2025· article· en· W4415436639 on OpenAlexaff
Sabyasachi Pramanik, N. Pradhan, N. Haldar, Sneha Samal, S. Suman

Bibliographic record

VenueMechanics of Solids · 2025
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsArtificial Intelligence in Medicine (Canada)
Fundersnot available
KeywordsViscoelasticityPoromechanicsGradationDissipative systemPhase velocityPorosityDispersion relationWave propagationRigidity (electromagnetism)

Abstract

fetched live from OpenAlex

Abstract This study presents a theoretical investigation of torsional wave propagation in a graded, fluid-saturated porous layer perfectly bonded to a Kelvin–Voigt viscoelastic half-space. The porous layer exhibits anisotropic behavior with spatially varying rigidity and density, capturing material gradation effects. A complex dispersion relation governing wave propagation is derived using the method of separation of variables, and the real and imaginary components are used to characterize phase velocity and attenuation, respectively. The influence of porosity, gradation, and viscoelastic damping on the dispersion characteristics is examined through numerical analysis. Results indicate that porosity enhances both phase and damping velocities due to fluid–solid coupling, while increasing material gradation leads to stiffer response and stronger attenuation. The model reduces to classical cases under specific parameter limits, providing a basis for validation and comparative assessment. The findings contribute to a deeper understanding of wave dynamics in heterogeneous and dissipative media, with implications for subsurface characterization, seismic analysis, and the design of functionally graded composites.

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 score0.082
Threshold uncertainty score0.585

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 head0.015
GPT teacher head0.256
Teacher spread0.241 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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