Class-C Simulations of LEAP-ASIA-2019 via OpenSees Platform by Using a Pressure Dependent Multi-yield Surface Model
Bibliographic record
Abstract
Abstract In this chapter, Class-C numerical simulations were performed for LEAP-ASIA-2019 centrifuge experiments that took place at different universities testing facilities. A comparative study was conducted among the simulated and experimental seismic responses of a mildly sloping ground of medium-dense to dense Ottawa-F65 sand under ramped sinusoidal acceleration input motions. A pressure dependent multi-yield surface model that can simulate the liquefaction potential of sand soils under earthquake loading was chosen for the numerical simulations through the OpenSees finite element modeling software. An initial calibration of the soil constitutive model, namely “Phase I,” was performed against different cyclic torsional shear tests for Ottawa-F65 sand under various Cyclic Stress Ratios (CSRs). Numerical modeling of centrifuge experiments “Phase II” was carried out after a few adjustments to the estimated model parameter values for the sake of providing proper computed output responses. The adopted soil model and simulation technique provide adequate numerical predictions of the liquefaction potential for the mildly sloping ground problem and accurately simulate the time histories of excess pore water pressure, accelerations, and surface deformations, regardless of experiencing a few undesirable responses for simulated Kyoto University centrifuge tests. The capabilities and limitations of the selected constitutive soil model and computational technique are analyzed and discussed through the context.
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 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".