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Record W2089879433 · doi:10.1520/jte20140083

Application of the Surface Wave Survey Method on Multi-Scale Engineering Problems: Laboratory and Field Testing Case Studies

2014· article· en· W2089879433 on OpenAlexaff
Kaoshan Dai, Xuehang Song, Xiaofeng Li, Zhenhua Huang, Yongdong Pan

Bibliographic record

VenueJournal of Testing and Evaluation · 2014
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Waves and Analysis
Canadian institutionsGeomechanica (Canada)
Fundersnot available
KeywordsField (mathematics)Scale (ratio)EngineeringMechanical engineeringMathematicsPhysics

Abstract

fetched live from OpenAlex

Abstract The geometric dispersion of surface waves offers the feature to infer the properties of a medium. The surface wave survey method is under continuous evolution and its applications include the noninvasive characterization of a medium at a small scale and the earthquake geotechnical survey at a large scale. Despite the significant difference in scales, these applications generally utilize the propagation of the surface waves along the boundary of a layered medium. This paper discusses standard steps involved in surface wave surveys. Two application case studies are conducted and presented in detail. In the small scale laboratory application case, a nondestructive testing (NDT) methodology is proposed for the coating investigation using laser interferometric measurements of surface waves. In the large scale field application, an accelerometer-based passive surface wave survey is designed for earthquake geotechnical engineering site characterization. Finally, recommendations for practical application of surface waves in multi-scale engineering problems are presented.

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)0.005
metaresearch head score (Gemma)0.005
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 score0.355
Threshold uncertainty score0.627

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.005
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 head0.105
GPT teacher head0.321
Teacher spread0.217 · 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

Citations4
Published2014
Admission routes1
Has abstractyes

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