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Record W2998779912 · doi:10.1061/9780784481479.017

Evaluation of the ISA-Hypoplasticity Constitutive Model for the LEAP-2017 Project

2018· article· en· W2998779912 on OpenAlexaboutno aff
W. Fuentes, Vicente Mercado, Carlos Lascarro

Bibliographic record

VenueGeotechnical Earthquake Engineering and Soil Dynamics V · 2018
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsnot available
Fundersnot available
KeywordsMonotonic functionLiquefactionConstitutive equationWork (physics)AnisotropyComputer scienceGeotechnical engineeringSet (abstract data type)Soil liquefactionStructural engineeringGeologyFinite element methodMechanical engineeringEngineeringMathematicsPhysics

Abstract

fetched live from OpenAlex

In this work the capabilities of the ISA-hypoplasticity model for the simulation of a sandy material are carefully analyzed. The ISA-hypoplasticity model is based on the hypoplastic model by von Wolfferdorff (1996), extended by the intergranular strain anisotropy (ISA) concept by Fuentes and Triantafyllidis (2015). The model is able to simulate monotonic and cyclic loading, and has been recently modified to consider the cyclic mobility effect. Within this work, a calibration procedure has been conducted to determine the material parameters of Ottawa F65 sand and a set of experiments showing monotonic and cyclic loading has been simulated. This work is embedded within the liquefaction experiments and analysis projects (LEAP), which seeks to assess and improve the current numerical tools for prediction and evaluation of soil liquefaction phenomena.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.025
GPT teacher head0.239
Teacher spread0.214 · 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

Citations3
Published2018
Admission routes1
Has abstractyes

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