MétaCan
Menu
Back to cohort
Record W2772150635 · doi:10.2749/222137817822208870

Fatigue Damage Identification in Precast Truss Girders Using Relative Wavelet Entropy

2017· article· en· W2772150635 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.

Bibliographic record

VenueReport · 2017
Typearticle
Languageen
FieldEngineering
TopicStructural Health Monitoring Techniques
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPrecast concreteGirderStructural engineeringTrussFibre-reinforced plasticWaveletPrestressed concreteTruss bridgeMaterials scienceComputer scienceEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

<p>An experimental implementation of a relative wavelet entropy (RWE)-based structural damage identification technique (DIT) is presented. The technique is capable of detecting and localizing structural damage, as well as estimating its severity, without the need for any data to be collected from undamaged (reference) state of structure. The bases of this reference-free DIT are: (1) structural damage changes the energy distribution of bridges’ vibrational signals; (2) these changes are detectable by means of discrete wavelet transforms (DWTs); and (3) the detected changes can be quantified using spectral entropy. The efficacy of the proposed RWE-based DIT in identification of structural damage has been verified through its application on a precast bridge truss girder system tested under fatigue loading. The girder consists of glass fibre-reinforced polymer (GFRP) tubes filled with concrete reinforced and connected to pretensioned top and bottom concrete chords by double-headed GFRP bars.‌</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.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.158
Threshold uncertainty score0.486

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 head0.069
GPT teacher head0.362
Teacher spread0.293 · 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