Probabilistic seismic liquefaction hazard mapping of western Metro Vancouver, British Columbia, Canada
Bibliographic record
Abstract
The Metro Vancouver region lies in a seismically active region with numerous areas exhibiting thick liquefiable layers and shallow groundwater levels. The liquefaction susceptibility classification is verified by considering the thickness of liquefiable layers from 808 cone penetration tests (CPT). Probabilistic seismic-induced liquefaction assessment based on the national seismic hazard model of the 2020 National Building Code of Canada is accomplished to account for the contribution of all seismic sources to the liquefaction evaluation. The liquefaction hazard curves from 900 CPT and shear wave velocity profiles are generated by incorporating the liquefaction potential index (LPI) and Ishihara-inspired liquefaction potential index (LPIISH) into the performance-based liquefaction assessment. The first probabilistic liquefaction hazard maps of Metro Vancouver are presented using the targeted return periods of 475 and 2,475 years. These liquefaction hazard maps reveal that liquefaction manifestations are expected in Richmond and Delta regions. In addition to considering seismic loading uncertainty, variability of soil resistance and the liquefaction triggering model are also accounted for through Monte Carlo simulation to embed the factor of safety against liquefaction (FSL) and the required soil improvement to prevent liquefaction (ΔqL) into the performance-based assessment. It yields the FSL and ΔqL hazard curves to determine the targeted design level. The probabilistic FSL, ΔqL, liquefaction return period and Hcr maps built on a large number of in situ data and semi-sophisticated analyses should be used together to mitigate against liquefaction hazard in the region. Useful regression models to predict ΔqL for return periods of 475 and 2,475 years from the soil resistance are developed. Deterministic analyses are performed for Cascadia subduction zone earthquakes using ground motion prediction equations of the 6th national seismic hazard model to determine the range of Mw-amax combinations that result in the observed paleo-liquefaction features in the region. A probabilistic framework is developed to account for the uncertainties of soil resistance, groundwater table depth, maximum ground acceleration, and the liquefaction model. Our probabilistic results also reveal that Cascadia interface earthquakes with Mw > 8.9 lead to a 31-57% probability of liquefaction triggering in the region. Magnitude-bound curves are also developed for Cascadia interface earthquakes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".