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Record W4415292530 · doi:10.2749/ghent.2025.1523

Feasibility Study on Structural Health Monitoring for Cantilevered Highway Sign

2025· article· W4415292530 on OpenAlexaff
Masayuki Saeki, Shingo Matsui, Aguru Kitahara

Bibliographic record

VenueReport · 2025
Typearticle
Language
FieldEngineering
TopicStructural Health Monitoring Techniques
Canadian institutionsNexen (Canada)
Fundersnot available
KeywordsCantileverStructural health monitoringAccelerometerVibrationNormal modeAccelerationSign (mathematics)

Abstract

fetched live from OpenAlex

<p>This paper describes the results of feasibility study on the structural health monitoring for the cantilevered highway signs. In this study, tri-axial accelerometers were installed on the cantilevered electronic highway signs in-service, and the acceleration responses were measured at six sites over a year. The measured data were analysed with ERA (Eigen-system Realization Algorithm) method to estimate the vibration modes. By clustering the estimated modes with k-means method, 4 commonly excited modes were identified. Furthermore, classification method was developed to determine the order of vibration modes estimated with ERA. The analysis result shows that the natural frequencies of the first and second modes are estimated with the accuracy of 0.003 Hz. It is thought that the accuracy is high enough to detect the changes in the natural frequencies due to the damage of cantilevered highway signs.</p>

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.138
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.072
GPT teacher head0.409
Teacher spread0.337 · 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.

Study designObservational
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
Published2025
Admission routes1
Has abstractyes

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