Site Response in a Layered Liquefiable Deposit: Evaluation of Different Numerical Tools and Methodologies with Centrifuge Experimental Results
Bibliographic record
Abstract
Results of a centrifuge experiment simulating seismic site response in a layered level liquefiable soil profile are used to evaluate and systematically compare the predictive capabilities of two common numerical platforms and two classes of advanced soil constitutive models (multiyield and bounding surface) used by three different teams. The pressure-dependent multiyield (PDMY02) and simple anisotropic SAND (SANISAND) constitutive models, implemented in open source and commercially available software, were independently calibrated by three teams using the same set of monotonic and cyclic triaxial test results. Class C predictions of the elastoplastic soil response in centrifuge following verification and initial calibration showed excessive dilative tendencies in all constitutive models to different degrees. These tendencies led to a notable overestimation of acceleration spikes at higher frequencies and an underestimation of net excess pore pressures in dense sand. The second calibration phase focused primarily on reducing soil’s dilative tendencies to match centrifuge tests, even at the cost of slightly sacrificing aspects of the response at an element level or abandoning the number of cycles to liquefaction. Despite differences in calibration methodologies and priorities among three modelers, the results show that small element tests and centrifuge experiments do not always lead to the same calibrated soil parameters. Further, although current numerical platforms and advanced constitutive models were capable of reproducing many aspects of seismic site response observed in the centrifuge, they still need fundamental improvements to capture volumetric settlements. This is an old problem that needs attention in future numerical and physical model studies.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.001 | 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".