Evaluation of Liquefaction Ejecta Potential from Case Histories and Insights from Nonlinear Dynamic Analyses
Bibliographic record
Abstract
High-quality data from 24 benchmark liquefaction case histories from Christchurch (New Zealand) and data from 123 global case histories were used to develop a family of empirical relationships for estimating the severity of liquefaction ejecta manifestation. The proposed empirical model provides means to develop liquefaction manifestation charts and estimate ejecta quantities (i.e., area covered by ejecta and ejecta-related settlement) as a function of the intensity of the earthquake excitation. The model is applicable to all types of liquefiable soil deposits within conventional liquefaction assessment using the cone penetration test. The principal factor discriminating between different ejecta potential is the soil deposit type. Deposits comprising loose liquefiable soils but interbedded with nonliquefiable soils have very limited ejecta potential. Such deposits have never exceeded 15% ejecta area coverage, based on current evidence. Conversely, deposits composed mostly of liquefiable soil show very high potential for extreme ejecta manifestation (up to 100% ejecta area coverage) when comprising loose critical layers (qc1Ncs(CL)≤110), though the severity of ejecta manifestation for such deposits substantially decreases with the increase in density of the critical layer (i.e., qc1Ncs(CL)). Results from evaluation of the model performance using 6,200 liquefaction case histories and recommendations for application of the model to engineering practice are presented and discussed. Results from nonlinear dynamic analyses are presented to provide further insights on the liquefaction response of deposits and their potential for ejecta manifestation.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".