Automatic Calibration Tool for Efficient Parameter Optimization of SANISAND Models under Cyclic Loading
Bibliographic record
Abstract
Constitutive modeling of cyclic liquefaction is challenging due to the complex soil behavior under cyclic loading. Aside from the predictive capability of the constitutive model, precise calibration of the model parameters is critical to accurately simulate the soil behavior in engineering applications. However, the calibration process for advanced constitutive soil models can be time-consuming and requires significant expertise. To address these challenges, an automatic parameter calibration tool (ACT) was developed to simplify the calibration process and reduce application hurdles. In this study, the performance of the ACT was evaluated on two versions of the SANISAND class of models with a focus on cyclic simple shear tests. Experimental data on Ottawa-F65 sand were used to establish calibration and prediction sets for the ACT. The results demonstrate the high efficiency of the ACT in calibrating the parameters within the limits of their simulative capabilities. The ACT significantly reduces the time and expertise required for calibration, making advanced soil models more accessible to engineers and researchers in the field of geotechnical engineering. The study provides a valuable contribution toward improving the accuracy and efficiency of cyclic liquefaction modeling.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".