Finite-Element Modeling of Continuous Surface Waves Tests: Numerical Accuracy with respect to Domain Size
Bibliographic record
Abstract
Near-field effects are inherent in continuous surface wave (CSW) tests. Such effects on the dispersion curves can be studied using the finite-element (FE) method. However, near-field effects may be obscured by boundary effects in a FE model. Although the use of local nonreflecting boundary conditions (NRBCs) may alleviate the problem, these need to be placed sufficiently far away to be effective. Quantitative guidelines on domain size with NRBCs are still lacking. This paper provides specific guidelines to determine the domain size in terms of the wavelength-to-domain size ratio, λ*/l*. With local NRBCs, numerical errors were found to be negligible when the domain size (l*) was extended to twice the longest wavelength (λ*). However, in modeling soft soil deposits in which shear-wave velocity (Vs) is less than 100 m/s, a smaller domain size (e.g., λ*/l*=2) is sufficient.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".