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Record W1997405168 · doi:10.1139/t09-127

Probabilistic framework for assessing liquefaction hazard at a given site in a specified exposure time using standard penetration testing

2010· article· en· W1997405168 on OpenAlexvenueno aff
C. Hsein Juang, Chang‐Yu Ou, Chih‐Chieh Lu, Zhe Luo

Bibliographic record

VenueCanadian Geotechnical Journal · 2010
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsnot available
Fundersnot available
KeywordsLiquefactionProbabilistic logicPenetration testStandard penetration testGeotechnical engineeringSoil liquefactionHazardSettlement (finance)Probabilistic analysis of algorithmsEngineeringReliability engineeringComputer scienceMathematicsStatistics

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.152
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.020
GPT teacher head0.238
Teacher spread0.217 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations20
Published2010
Admission routes1
Has abstractyes

Explore more

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