Enhancing accuracy and efficiency in cyclic liquefaction modeling: An automatic calibration framework for advanced constitutive models
Bibliographic record
Abstract
Precise calibration of constitutive models for cyclic liquefaction is essential but often time-consuming and requires significant expertise, limiting broader application in geotechnical practice. This paper introduces an automatic calibration tool designed to streamline the process for advanced constitutive models under both monotonic and cyclic loading. The tool supports various types of monotonic and cyclic laboratory test data, offers multiple choices of suitable comparison planes for error calculation, with a focus to also suit cyclic liquefaction problems, and employs advanced optimization techniques. The calibration follows a two-stage approach: first, optimizing parameters governing monotonic response using monotonic test data; second, refining these and additional parameters with both monotonic and cyclic data. The critical state parameters are fixed throughout, while the elasticity parameters are fixed in the second stage, all within defined bounds. Using this automatic calibration tool and the adapted calibration strategy, extensive element-level test data was used to determine the parameters of the SANISAND-MSf model for a given sand. These calibrated parameters were then used to simulate boundary value problems , including centrifuge tests of liquefiable sand slopes and sheet-pile-supported liquefiable sand deposits, all subjected to base excitations, demonstrating excellent alignment with experimental results. This validation highlights the robustness, reproducibility, and accuracy of the tool to model cyclic liquefaction while significantly reducing the expertise and time required for calibration. This represents a significant advancement toward the broader adoption of advanced constitutive soil models in geotechnical engineering practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".