Shear strain dependent amplification function of earth dam‐flexible canyon system by the hybrid FEM‐SBFEM technique
Bibliographic record
Abstract
Abstract The finite element method (FEM) is a powerful tool for the nonlinear modeling of dynamic problems. In the present work, the equivalent linear method (EQL) has been implemented into the FEM. For stratifying the radiation damping condition and rigorously modeling canyon as an elastic unbounded domain, the scaled boundary finite element method (SBFEM) was utilized. The FEM‐SBFEM technique, wherein FEM is coupled with SBFEM, has been extended to take into consideration the effect of earth dam material nonlinear behavior. It was observed that the nonlinear behavior greatly affects the natural frequency, the amplification function (AF), and peak crest acceleration of the earth dam located in canyons. The effects of canyon geometry and flexibility on the nonlinear behavior were examined, and it was seen that by increasing the flexibility of the canyon, the effect of nonlinearity was decreased. The El Infiernillo dam was modeled by 3D proposed technique, and a comparison of the crest AF obtained by the proposed method with the recorded data shows the accuracy of the methodology.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".