Probabilistic framework for assessing liquefaction hazard at a given site in a specified exposure time using standard penetration testing
Bibliographic record
Abstract
This paper presents a probabilistic framework for assessing the liquefaction hazard at a given site in a given exposure time. Ten sites from different seismic-prone regions of the USA are studied to validate the developed probabilistic framework. Additionally, this framework is extended from the focus of liquefaction potential at a given soil element (or finite layer) to the concern of the whole soil column based on the concept of liquefaction potential index (LPI) proposed by Iwasaki and his co-workers. In this extended framework, the probability of surface manifestation of liquefaction at a given site subjected to all ground motions at all hazard levels in a given exposure time is determined. As an example to illustrate this probabilistic framework, the widely used, standard penetration test (SPT)-based method by Youd et al. is adopted as a building block in the framework. This framework is illustrated with examples and its versatility is demonstrated. Finally, the procedure for extending the developed framework to the evaluation of ground settlement is outlined.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 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".