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Record W1995703106 · doi:10.1016/j.sbsr.2015.04.003

Sensing linear viscoelastic constitutive parameters with a Timoshenko beam on a multi-layer foundation: Modeling and simulation

2015· article· en· W1995703106 on OpenAlexafffund
Javad Fattahi, Davide Spinello

Bibliographic record

VenueSensing and Bio-Sensing Research · 2015
Typearticle
Languageen
FieldEngineering
TopicStructural Health Monitoring Techniques
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsViscoelasticityTimoshenko beam theoryConstitutive equationMaterials scienceBeam (structure)Standard linear solid modelFinite element methodMechanicsDeformation (meteorology)Coupling (piping)Substrate (aquarium)Structural engineeringComposite materialPhysicsEngineeringGeology

Abstract

fetched live from OpenAlex

We present a sensor model comprised of a Timoshenko beam coupled with a linear viscoelastic substrate via a distributed system of compliant elements. The system of governing equations includes the evolution of the kinematic descriptors of the Timoshenko beam and of the interface between the coupling elements and the viscoelastic substrate. This model is used to pose an inverse problem aimed at estimating the constitutive parameters of the substrate from deformation measurements of the beam. The sensing model is demonstrated by comparing its prediction with published experimentally obtained constitutive parameters identifying standard linear viscoelastic material models, showing good agreement between model estimations and experimental results.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0020.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.186
GPT teacher head0.403
Teacher spread0.216 · 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 source (direct Gemma or distilled Codex), 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

Citations3
Published2015
Admission routes2
Has abstractyes

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