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Record W2327064654 · doi:10.1061/40786(165)6

Multi-Plane Model for Soil Liquefaction

2005· article· en· W2327064654 on OpenAlexafffund
Sung-Sik Park, Peter Byrne

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGeotechnical engineeringLiquefactionMonotonic functionConstitutive equationPlane stressSoil liquefactionAnisotropyShear (geology)GeologySimple shearMechanicsStructural engineeringEngineeringMathematicsFinite element methodPhysicsMathematical analysis

Abstract

fetched live from OpenAlex

A simplified constitutive model based on a multi-plane concept is presented for monotonic and cyclic soil response including liquefaction. The proposed model uses only two mobilized planes; a maximum shear stress plane, and a horizontal plane. The application of more than one mobilized plane can intrinsically handle anisotropy as well as rotation of principal planes without additional parameters. The soil skeleton behaviour observed in cyclic drained simple shear tests, including compaction during unloading and dilation at large strain is considered in the model. Undrained and partially drained monotonic and cyclic response can be predicted by considering the volumetric constraint of the water on the drained or skeleton behaviour. The rotation effect associated with simple shear loading from a K0 consolidated state is incorporated in the proposed model. This constitutive model is incorporated into the dynamic coupled stress-flow finite difference program FLAC (Fast Lagrangian Analysis of Continua). The model is calibrated by capturing the undrained cyclic behaviour of Fraser River sand, and can be used in the design of liquefaction remediation measures involving densification and drainage.

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)0.000
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: none
Teacher disagreement score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.017
GPT teacher head0.216
Teacher spread0.199 · 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

Citations0
Published2005
Admission routes2
Has abstractyes

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