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Forward Modeling of Steady-State Surface Wave Test

2005· article· en· W1970367768 on OpenAlexaff
Maud Storme, Richard Fortier, Jean‐Marie Konrad

Bibliographic record

VenueJournal of Geotechnical and Geoenvironmental Engineering · 2005
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsStiffness matrixStiffnessDirect stiffness methodSurface waveDispersion (optics)Matrix (chemical analysis)Representation (politics)WavelengthMathematical analysisFinite element methodRayleigh waveMathematicsSurface (topology)MechanicsStructural engineeringGeometryPhysicsEngineeringOpticsMaterials science

Abstract

fetched live from OpenAlex

The forward modeling of the dispersion curve of a layered medium calculated from the spectral characteristics of Rayleigh waves requires an efficient algorithm for predicting the surface displacements of the medium under a dynamic load. The usual methods for solving the wave equation are based on matrix methods prone to numerical problem or yielding to the determination of complex modes that are difficult to interpret. A modified linearized stiffness matrix method is proposed in this technical note to solve the discrete stiffness matrix. The developed algorithm is based on a finite element equation associating each surface wave mode to the resonant frequency of a structure with a stiffness depending on the wavelength. The filtering of complex wave modes to keep only suitable surface waves is avoided. Furthermore, the mesh design is based on the actual wavelength of each mode providing a better representation of higher modes. The new algorithm is validated with two irregular stiffness profiles.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.685

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 head0.007
GPT teacher head0.181
Teacher spread0.174 · 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

Citations3
Published2005
Admission routes1
Has abstractyes

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