Performance-based liquefaction analysis and probabilistic liquefaction hazard mapping using CPT data within the Fraser River delta, Canada
Bibliographic record
Abstract
Since the Fraser River delta is affected by multiple seismic sources, the selection of a single combination of magnitude and maximum ground acceleration to evaluate liquefaction initiation is challenging. In this study, we use a probabilistic seismic hazard analysis to account for all earthquake scenarios and also consider the liquefaction model uncertainty and soil resistance uncertainty using 787 CPT data across the study area. Hazard curves are generated for the factor of safety against liquefaction (FS L ) and the amount of required soil improvement to prevent liquefaction (Δq L ) which provide a comprehensive assessment of liquefaction triggering. We derive the FS L and Δq L values corresponding to return periods of 475 and 2475 years and generate the first probabilistic liquefaction hazard mapping for the region. Most of the lowland areas of Metro Vancouver correspond to low FS L and therefore soil improvement is needed for liquefaction mitigation. • A performance-based liquefaction analysis accounts for all earthquake hazards. • Key parameter uncertainities are accounted for in the presented probabilistic liquefaction methodology. • The probabilistic liquefaction hazard mapping offers a preliminary assessment for soil improvement applications.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".