Uncertainty in Liquefaction-Induced Settlement in Numerical Simulations due to Model Calibration
Bibliographic record
Abstract
Liquefaction-induced reconsolidation settlements occur as earthquake-generated excess pore pressures dissipate and can lead to significant damage to overlying infrastructure as well as buried structures. Design of earthquake resilient infrastructure in areas with high liquefaction susceptibility requires a methodology to predict these settlements for a variety of soil types and boundary conditions. The state-of-practice empirical models that are commonly used to compute settlements exhibit several limitations that might affect the accuracy of predicted settlement, including an inability to include the effects of partial drainage, non-liquefiable crust, thin layers, and soil fabric. Numerical models can overcome some of these limitations but require proper calibration and validation. Centrifuge and shaking table experiments can be used for this validation, but multiple challenges arise when comparing numerical simulations and experimental results for liquefaction-induced settlements. This paper describes the need for soil-specific calibration of reconsolidation behavior which is one of the key challenges in accurate prediction of settlements. Multiple experiments considering free-field conditions are simulated using the numerical platform FLAC and the constitutive model PM4Sand. The bias in predicted settlement using the default PM4Sand reconsolidation formulation is assessed. A new, tentative relationship for the PM4Sand reconsolidation parameter, fsed,min, is proposed by incorporating the mean grain diameter, D50. Settlement results from simulations using the default and modified reconsolidation schemes are compared to the experiments to assess the level of agreement between them.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".