Probabilistic Liquefaction Triggering and Manifestation Models Based on Cumulative Absolute Velocity
Bibliographic record
Abstract
This study proposes the capacity cumulative absolute velocity (CAVc) as a novel measure for the resistance of granular soils to earthquake-induced liquefaction. The CAVc is defined as the cumulative absolute velocity (CAV) needed to generate a threshold excess pore pressure ratio (ru) value at a specified depth in a layer of liquefiable soil. A probabilistic model for predicting CAVc corresponding to ru values between 0.5 and 1.0 is developed using a database of over 280,000 estimates of CAVc from one-dimensional (1D), nonlinear, effective stress site-response analyses. These models provide CAVc as a function of depth, the presence and location of low-permeability interlayers in the soil profile, and soil stiffness (as reflected in the normalized cone tip resistance from cone penetration test results). The standard deviation around the model’s estimates of CAVc ranges from 0.59 natural log units for an ru threshold of 1.0 to 0.83 natural log units for an ru threshold of 0.5. Correlation models are provided for predicting CAVc throughout the depth of a soil profile, across multiple ru thresholds, or both. Finally, CAVc is implemented in a proposed modification of the liquefaction potential index (LPICAV). The new index is validated using data from three earthquakes in Canterbury, New Zealand, and has a slightly improved predictive capability compared to existing indices, while making use of a relatively predictable intensity measure (i.e., CAV of the outcropping rock motion); this intensity measure is also compatible with performance-based methods for predicting liquefaction consequences. Finally, a guide for model implementation and examples of various applications are provided.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | high |
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".