Calibration of an elastoplastic model of sand liquefaction using the swarm intelligence with a multi-objective function
Bibliographic record
Abstract
According to post-seismic observations, spectacular examples of engineering failures can be ascribed to the occurrence of sand liquefaction, where a sandy soil stratum could undergo a transient loss of shear strength and even behave as a “liquid”. Therefore, correct simulation of liquefaction response has become a challenging issue in geotechnical engineering field. In advanced elastoplastic models of sand liquefaction, certain fitting parameters have a remarkable effect on the computed results. However, the identification of these parameters, based on the experimental data, is usually intractable and sometimes follows a subjective trial-and-error procedure. For this, this paper presented a novel calibration methodology based on an optimization algorithm (particle swarm optimization (PSO)) for an advanced elastoplastic constitutive model. A multi-objective function was designed to adjust the global quality for both monotonic and cyclic triaxial simulations. To overcome computational problem probably appearing in simulation of the cyclic triaxial test, two interrupt mechanisms were designed to prevent the particles from wasting time in searching the unreasonable space of candidate solutions. The Dafalias model has been used as an example to demonstrate the main programme. With the calibrated parameters for the HN31 sand, the computed results were highly consistent with the laboratory experiments (including monotonic triaxial tests under different confining pressures and cyclic triaxial tests in two loading modes). Finally, an extension example is given for Ottawa sand F65, suggesting that the proposed platform is versatile and can be easily customized to meet different practical needs.
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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 | 0.001 |
| 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".