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Record W1503697433 · doi:10.1109/icsmc.2005.1571685

A Novel Approach for Detection of Damage in Adhesivelybonded Joints in Plastic Pipes Based on Vibration Method Using Piezoelectric Sensors

2006· article· en· W1503697433 on OpenAlexaff
N. Cheraghi, Mark Riley, Farid Taheri‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicStructural Health Monitoring Techniques
Canadian institutionsDalhousie University
Fundersnot available
KeywordsVibrationHilbert–Huang transformStructural engineeringModalFourier transformMaterials scienceWaveletAcousticsStructural health monitoringComputer scienceWavelet transformEngineeringComposite materialMathematicsFilter (signal processing)Artificial intelligencePhysics

Abstract

fetched live from OpenAlex

The use of dynamic response to identify damage in engineering structures has been the focus of several research works in the recent years. Most of the vibration-based damage assessment methods developed thus far require modal properties that are obtained via the traditional Fourier transform (FT). Unfortunately, the Fourier-based modal properties, such as natural frequencies, mode shapes, etc., have been reported to be mainly insensitive to structural damage, and hence are not regarded as suitable damage indicators. This paper discusses the application of piezoelectric sensors used for the evaluation of integrity of adhesively bonded joints in PVC plastic pipes. A systematic experimental and analytical investigation was carried out to demonstrate the integrity of adhesively bonded joints. Besides the commonly used methods, a newly emerging time-frequency method, namely the empirical mode decomposition (EMD), is also employed. Two novel "damage indices" are developed based on the evaluation of vibration signals with the use of EMD and the fast Fourier integration induced energies. The results are compared to the available damage index method based on the wavelet packet transformation (WPT), which has been used in the literature review. As it will be seen, by use of the damage indices, one could effectively detect the integrity of the adhesively bonded joints. Moreover, the energy indices can distinguish the differences among disbond densities in the joints.

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 score0.196
Threshold uncertainty score0.566

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.028
GPT teacher head0.287
Teacher spread0.259 · 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

Citations20
Published2006
Admission routes1
Has abstractyes

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