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Structural Vibration Self-sensing Piezoelectric Device

2023· article· en· W4386077106 on OpenAlexaff
Muath Bani-Hani, Nabil Nahas, Mohammed Abouheaf, Sallam A. Kouritem, Yasmin Ayman

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicInnovative Energy Harvesting Technologies
Canadian institutionsUniversité de Moncton
Fundersnot available
KeywordsPiezoelectricityVibrationAcousticsComputer scienceMaterials sciencePhysics

Abstract

fetched live from OpenAlex

This paper aims to harvest the wasted structural mechanical vibrations and convert them to functioning power to operate sensors in remote and inaccessible locations for structural health monitoring purposes. The proposed sensing device is a uniform simply supported composite-piezoelectric beam. The energy effectiveness of earthquake-induced structural vibrations is presented. The proposed sensor can be employed in structural health monitoring to transmit important information like earthquake-induced structural vibrations. The analytical modelling is presented based on Euler-Bernoulli’s theory. The analytical results are then numerically verified using FEM results. MATLAB is employed to simulate the analytical results and COMSOL multi-physics is employed to model and simulate numerically the proposed sensing device.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.724
Threshold uncertainty score0.365

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
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 head0.018
GPT teacher head0.226
Teacher spread0.208 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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