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Record W2163409318 · doi:10.6310/jog.2006.1(1).2

Liquefaction potential index: A critical assessment using probability concept

2006· article· en· W2163409318 on OpenAlexaff
David Kun Li, C. Hsein Juang, Ronald D. Andrus

Bibliographic record

VenueJournal of geoengineering · 2006
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsGolder Associates (Canada)
Fundersnot available
KeywordsLiquefactionPenetration testGeotechnical engineeringCone penetration testSoil liquefactionIndex (typography)Foundation (evidence)Environmental scienceEngineeringGeologyComputer scienceGeography

Abstract

fetched live from OpenAlex

Liquefaction potential index (IL) was developed by Iwasaki et al. in 1978 to predict the potential of liquefaction to cause foundation damage at a site. The index attempted to provide a measure of the severity of liquefaction, and according to its developer, liquefaction risk is very high if IL > 15, and liquefaction risk is low if IL ≤ 5. Whereas the simplified procedure originated by Seed and Idriss in 1971 predicts what will happen to a soil element, the IL predicts the performance of the whole soil column and the consequence of liquefaction at the ground surface. Several applications of the IL have been reported by engineers in Japan, Taiwan, and the United States, although the index has not been evaluated extensively. In this paper, the IL is critically assessed for its use in conjunction with a cone penetration test (CPT)-based simplified method for liquefaction evaluation. Emphasis of the paper is placed on the appropriateness of the formulation of the index IL and the calibration of this index with a database of case histories. To this end, the framework of IL by Iwasaki et al. is maintained but the effect of using different models of a key component in the formulation is explored. The results of the calibration of IL are presented. Moreover, use of IL is extended by introducing an empirical formula for assessing the probability of liquefaction-induced ground failure.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.584
Threshold uncertainty score0.822

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.001
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 head0.008
GPT teacher head0.235
Teacher spread0.228 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations26
Published2006
Admission routes1
Has abstractyes

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