Numerical study on the influence of near-fault and far-fault earthquakes on a subway station with emphasis to scattering on the wave propagation
Bibliographic record
Abstract
To improve the design of surface structures built on underground structures to be safe and resistant to earthquake, it is necessary to study the effects of the underground structures on the scattering in wave propagation and the surface ground acceleration. To this end, various parameters must be studied, including input motion, embedment of the structure, and different subway station dimensions. The present study focuses on these parameters by employing a nonlinear cyclic model called ARCS to simulate the shear modulus reduction and damping ratio increase of soil corresponding to the ones given by the user. The variations of the spectral ratio and the affected period range, peak ground acceleration, and peak relative lateral displacement versus the relative distance under near-fault and far-fault earthquakes are presented. The results indicate that different amplification or de-amplification effects at different surface positions were produced at each frequency by appearing a significant influence on the dynamic behavior of ground surface, soil layers, and the surface structure when a subway station is present. A 1.3 times increase in the surface ground acceleration subjected to far-fault earthquakes and a 1.6 times increase in the relative displacement indicated that study parameters have significant influences on the amplification ratio and scattering of the wave.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".