Comparison of empirical seismic lateral spread displacement models based on probabilistic geotechnical hazard curves developed for Canada
Bibliographic record
Abstract
Case histories have reported significant damage to structures during earthquakes due to permanent ground displacements (PGDs) arising from liquefaction-induced lateral spreading. The empirical model by Youd et al. (2002) is classical and has been widely adopted in practice over two decades owing to its simplicity. However, the suitability of this model for M>8 earthquakes is at stake, as it is largely based on datasets with M<8 earthquakes. Zhang and Zhao (2005) proposed another empirical model to compute PGDs that accounts for the underlying mechanism and different tectonic source type responsible for earthquake of a given magnitude; accordingly, the outcomes from this model differentiates between crustal and subduction earthquakes. The authors in their recent work developed probabilistic lateral spread displacement hazard curves by implementing Youd et al. (2002) and Zhang and Zhao (2005) models using the Openquake opensource platform, where the users are provided with a choice of using hazard curves based on the model of their preference. Based on the initial comparison of the hazard curves, it has been observed that both the models show similar trends for all the geographic locations considered in the study. However, the annual probability of occurrence reported by Zhang and Zhao (2005) model were found to be significantly higher than those predicted by Youd et al. (2002), for a given level of expected lateral spread displacement. This apparent conservatism of the predictions based on Zhang and Zhao (2005) motivated the authors to perform a comparative assessment in order to understand the underlying reasons for the differences in the outcomes and comment on the applicability of the models. In view of this, Openquake analyses were performed considering some selected geographic locations in Canada, and the findings from this work are presented herein.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".