Motazedian D. Probabilistic liquefaction hazard analysis for four Canadian cities. Bulletin of the Seismological Society of America 2011;101:190–201
Bibliographic record
Abstract
Abstract Changes to building codes in the last decade, lowering the probability at which design ground motions for geotechnical applications are defined, have led to an urgent need for a probabilistic approach/tool for liquefaction potential assessment. We propose a consistent approach for probabilistic liquefaction hazard analysis (PLHA) that is based on probabilistic seismic hazard analysis and incorporates a reliability-based liquefaction potential evaluation method based on shear-wave velocity data. The method directly takes the joint probability distribution of peak ground accelera-tion and moment magnitude into account. We demonstrate the method for four Canadian cities, employing our interim updated seismic hazard models for eastern and western Canada. Using the developed method and representative site profiles, PLHA is implemented for four major cities across Canada with the aim of investigating the impact of regional seismic characteristics on liquefaction hazard assessment. Sensitivity analysis indicates that different magnitude ranges of dominant contributing seismic events have significant impact on the extent of liquefaction hazard. More specifically, for a given seismic excitation level, the relatively high hazard contribu-tions from small-to-moderate earthquakes in eastern Canada leads to less significant liquefaction potential, in comparison with similar sites in western Canada.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".