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Record W2079336873 · doi:10.1177/1045389x06061130

The Coupled Dynamic Behavior of Piezoelectric Sensors Bonded to Elastic Media

2006· article· en· W2079336873 on OpenAlexaff
X. D. Wang, Guoliang Huang

Bibliographic record

VenueJournal of Intelligent Material Systems and Structures · 2006
Typearticle
Languageen
FieldEngineering
TopicUltrasonics and Acoustic Wave Propagation
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPiezoelectricityPiezoelectric sensorMaterials scienceFinite element methodStiffnessAcousticsCoupling (piping)Deformation (meteorology)Structural engineeringFourier transformTransformation (genetics)Sensitivity (control systems)Plane (geometry)Electronic engineeringEngineeringComposite materialGeometryMathematical analysisPhysicsMathematics

Abstract

fetched live from OpenAlex

Surface-bonded piezoelectric sensors can be used to monitor the mechanical behavior of structures for damage detection. This article provides a comprehensive theoretical study of the dynamic coupling between a surface piezoelectric sensor and an elastic half-plane. Attention is focused on the transformation of mechanical deformation into electric signals under dynamic loads. The effect of the longitudinal stiffness of the sensor is included in the developed sensor model. The problem is then formulated by using Fourier transform and solving the resulting integral equations in terms of the interfacial stress. The accuracy of the developed sensor model is evaluated by comparing with results from the finite element analysis. Numerical simulation is conducted to study the relation between the sensor response and the deformation of the host medium under static and dynamic loads. The results indicate the significant effects of the geometry of the sensor, the material mismatch of the system, and the loading frequency upon the sensor response.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

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 communication0.0000.001
Open science0.0010.001
Research integrity0.0010.000
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 head0.004
GPT teacher head0.200
Teacher spread0.196 · 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 designBench or experimental
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

Citations35
Published2006
Admission routes1
Has abstractyes

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