Total Stress Analysis of Soft Clay Ground Response in Centrifuge Models
Bibliographic record
Abstract
This paper presents one-dimensional ground response simulations of centrifuge models involving soft clay deposits subjected to ground motions of varying intensity. Total stress ground response simulations were performed using equivalent-linear (EL) and nonlinear (NL) methods. Shear strains higher than 10% were mobilized during large ground motions; therefore, undrained shear strength of the clay is an important parameter for the simulations. Testing shows that the San Francisco Bay Mud materials used in centrifuge modeling have monotonic shear strengths that increase by 13% per log cycle of shear strain rate. A comparison of simulation results to observations reveals the importance of incorporating shear strength into the development of stress-strain backbone curves, with appropriate consideration of rate adjustments to shear strength and stiffness. NL ground response simulations provide a good match to observed pseudospectral accelerations only when rate-adjusted shear strengths are properly accounted for; otherwise, the NL simulations have significant underprediction bias at oscillator periods less than the soil column period. EL modeling, even with the incorporation of shear strength, leads to unrealistic spectral shapes and overprediction at short spectral periods for tests involving large-strain site response.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".