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Record W2036921532 · doi:10.1088/0964-1726/18/4/045005

Parametric identification of a transmission line model for<i>in situ</i>damage characterization in lap joints

2009· article· en· W2036921532 on OpenAlexafffund
Dany Francoeur, Philippe Micheau, Patrice Masson

Bibliographic record

VenueSmart Materials and Structures · 2009
Typearticle
Languageen
FieldEngineering
TopicUltrasonics and Acoustic Wave Propagation
Canadian institutionsUniversité de Sherbrooke
FundersNatural Sciences and Engineering Research Council of CanadaFonds Québécois de la Recherche sur la Nature et les Technologies
KeywordsLap jointJoint (building)Reflection coefficientReflection (computer programming)Transmission lineStructural engineeringParametric statisticsCalibrationLine (geometry)AcousticsMaterials scienceBendingTransmission coefficientTransmission (telecommunications)OpticsEngineeringComputer scienceMathematicsGeometryPhysicsTelecommunications

Abstract

fetched live from OpenAlex

This paper presents an in situ damage identification method to characterize the thickness and location of a notch in a one-dimensional lap joint. The approach uses the propagation of flexural waves generated by a piezoceramic (PZT) to evaluate the global reflection coefficient of a complex structure such as a lap joint. A transmission line model (TLM) is used to describe the theoretical reflection coefficient from healthy and damaged lap joints. Parameters from the lap joint are identified in two steps from the experimental measurement of the reflection coefficient. The first step is a calibration step which gives, for the healthy lap joint, the length of the lap joint and distance between the measurement point and the lap joint. The identification of these parameters is performed by minimizing a cost function evaluated for different combinations of parameters. The second step allows us to obtain the parameters associated with the notch, which are its location and thickness. Several cases of healthy and damaged lap joints are conducted and very good results are achieved for the identification of the parameters.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.355
Threshold uncertainty score0.295

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 head0.009
GPT teacher head0.220
Teacher spread0.210 · 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 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

Citations3
Published2009
Admission routes2
Has abstractyes

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