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Record W4392125316 · doi:10.1007/978-3-031-48821-4_17

LEAP-ASIA-2019 Centrifuge Test Simulations of Liquefiable Sloping Ground

2024· book-chapter· en· W4392125316 on OpenAlexaboutno aff
Zhijian Qiu, Ahmed Elgamal

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsnot available
Fundersnot available
KeywordsCentrifugeLiquefactionGeotechnical engineeringDilatantGeologyConstitutive equationShear (geology)Triaxial shear testShear stressPore water pressureDeformation (meteorology)MechanicsEngineeringStructural engineeringFinite element methodPhysics

Abstract

fetched live from OpenAlex

Abstract Numerical simulations of a liquefiable sloping ground for LEAP-ASIA-2019 centrifuge tests are presented. The simulations are performed using a pressure-dependent constitutive model implemented with the characteristics of dilatancy, cyclic mobility, and associated shear deformation. The soil model parameters are determined based on a series of stress-controlled cyclic torsional shear tests of Ottawa F-65 sand with relative density Dr = 60% during calibration phase. Computational framework for the seismic response analysis is discussed and the computed results are presented for all selected centrifuge experiments during Type-C phase. Measured time histories of these experiments are reasonably captured. It is demonstrated that the pressure-dependent constitutive model as well as the overall employed computational framework has the potential to predict the response of the liquefiable sloping ground, and subsequently realistically evaluates the performance of an equivalent soil system subjected to seismically induced liquefaction.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.009
GPT teacher head0.192
Teacher spread0.183 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2024
Admission routes1
Has abstractyes

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