Improving the accuracy of soil-structure interaction analysis through the generalized subtraction method
Bibliographic record
Abstract
This study proposes the Generalized Subtraction Method (GSM) to improve the accuracy of Soil-Structure Interaction (SSI) analysis, which is crucial for seismic design of large structures such as nuclear power plants. Although the existing Subtraction Method (SM) used in the SASSI program is advantageous in terms of computational efficiency, the method has limitations that can cause abnormal responses in high-frequency regions. To address this issue, this study introduces a method of defining additional interaction nodes in the excavated soil to shift the spurious fundamental natural frequency of the excavated soil with fixed boundary conditions at the interaction nodes to above the maximum frequency of interest. By adjusting the fundamental natural frequency through iterative eigenvalue analysis, the proposed method provides stable and accurate SSI analysis results even in high-frequency regions. Numerical analysis results for two example models showed that the GSM achieved a similar level of accuracy to the Direct Method (DM) while using fewer interaction nodes than the existing Modified Subtraction Method (MSM).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".