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Improving the accuracy of soil-structure interaction analysis through the generalized subtraction method

2025· article· en· W4411256223 on OpenAlexaff
H. K. Lee, Oh‐Sung Kwon, Jae Min Kim

Bibliographic record

VenueNuclear Engineering and Design · 2025
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Analysis
Canadian institutionsUniversity of Toronto
FundersMinistry of Trade, Industry and Energy
KeywordsSubtractionMathematicsArithmetic

Abstract

fetched live from OpenAlex

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).

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: none
Teacher disagreement score0.954
Threshold uncertainty score0.441

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.009
GPT teacher head0.236
Teacher spread0.227 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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