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Record W3012715516 · doi:10.1139/cgj-2019-0167

Dyadic wavelet analysis of bender element signals in determining shear wave velocity

2020· article· en· W3012715516 on OpenAlexvenueno aff
Chen Guan, Fangtong Wang, Dianqing Li, Yong Liu

Bibliographic record

VenueCanadian Geotechnical Journal · 2020
Typearticle
Languageen
FieldEngineering
TopicUltrasonics and Acoustic Wave Propagation
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsMaximaWavelet transformWaveletShear modulusShear (geology)Continuous wavelet transformWave velocityAcousticsMathematicsMathematical analysisGeologyMaterials scienceDiscrete wavelet transformPhysicsComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Determining shear wave velocity is a critical technique in bender element tests, as it can be readily affected by near-field effects, wave reflection, and other factors. This study proposes a new method based on the dyadic wavelet transform modulus maxima. Combining the local modulus maxima of dyadic wavelet transform approximate coefficients at fine decomposition levels and an appropriate threshold value, the proposed method can automatically detect the target point. For validation, a comparative study among the dyadic wavelet transform modulus maxima, peak-to-peak, first arrival, and cross-correlation methods was carried out using 140 sets of bender element signals. The comparison results show that the proposed method not only mitigates the adverse effects of near-field, later major peaks, and noise contamination, but is also more robust in estimating shear wave velocity.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.021
GPT teacher head0.212
Teacher spread0.191 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations14
Published2020
Admission routes1
Has abstractyes

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