MétaCan
Menu
Back to cohort
Record W4317496707 · doi:10.1109/tim.2023.3238033

A Time-Domain Method for Ultrasound Concrete Health Monitoring Using In Situ Piezoelectric Transducers

2023· article· en· W4317496707 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Transactions on Instrumentation and Measurement · 2023
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Resonator Technologies
Canadian institutionsSimon Fraser University
FundersMitacs
KeywordsTransducerNormalization (sociology)AcousticsTime domainPiezoelectricityPiezoelectric sensorStructural health monitoringUltrasonic sensorSIGNAL (programming language)PMUTFrequency domainMaterials scienceElectronic engineeringStructural engineeringComputer scienceEngineeringPhysics

Abstract

fetched live from OpenAlex

In this article, a smart time-domain technique is presented for detecting flaws in concrete structures using embedded piezoelectric transducers. The method relies on the time-of-flight of ultrasound (US) waves generated and sensed by piezoelectric transducers which are embedded in a concrete structure before pouring the concrete. A damage index is introduced to quantify the likelihood of defects in the region between the two probes. The index is comprised of a normalization function that is independent of the strength of the captured signal and not sensitive with respect to the amount of coupling between the embedded sensors and the structure. A sliding mode extremum-seeking (SMES) method is utilized for real time calculation of the damage index. The proposed health monitoring system was verified by experimental results. Comparisons are performed when using a conventional approach by a grid search method to demonstrate performance improvement of the proposed system.

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.

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)0.001
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: none
Teacher disagreement score0.674
Threshold uncertainty score0.731

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.046
GPT teacher head0.299
Teacher spread0.252 · 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