Dynamic Soil–Structure-Group Interaction Under Incident Antiplane Shear Waves: Analytical Solution and Parametric Analysis
Bibliographic record
Abstract
Dynamic soil–structure-group interaction (SSGI) effect is of great significance for seismic analysis and design of structural portfolios in densely built areas. However, previous studies investigating the SSGI effect fail to rigorously disclose the fundamental physics and comprehensively assess the influences of various affecting parameters. Based on elastic wave diffraction theory, this study considers the single-degree-of-freedom (SDOF) structures with rigid circular-arc foundations buried in elastic semi-space and derives the analytical solution to quantify the SSGI under antiplane shear waves, and solves the solution by MATLAB software. The proposed solution can solve the dynamic interaction between soil and an arbitrary number of structures. Its accuracy is also examined against previous studies for simple structure group scenarios. Subsequently, a comprehensive set of parametric analyses is performed to quantify the influences on the structural response due to the presence of adjacent structures. This study indicates that the SSGI effect is highly dependent on the frequency and incident angle of the wave, the structural number, natural frequencies, and spacing, the soil shear wave velocity, and the depth and size of foundation. Greater structural damping generally reduces the SSGI effect, though the magnitude of this reduction is minimal. In addition, the SSGI effect does not always increase with a larger number of structures, it fluctuates and tends to decay with a larger structure spacing.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".