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Record W3000009530 · doi:10.1061/9780784481479.002

A Constitutive Model Controlling Damping for 2D and 3D Site Response

2018· article· en· W3000009530 on OpenAlexaff
Samuel Yniesta, Scott J. Brandenberg

Bibliographic record

VenueGeotechnical Earthquake Engineering and Soil Dynamics V · 2018
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsPolytechnique Montréal
FundersNational Science Foundation
KeywordsReduction (mathematics)GeneralizationShear modulusConstitutive equationModulusNonlinear systemPlane stressStructural engineeringStress (linguistics)Response analysisShear stressShear (geology)Damping ratioMaterials scienceMechanicsMathematical analysisMathematicsEngineeringGeometryPhysicsFinite element methodAcousticsComposite materialVibration

Abstract

fetched live from OpenAlex

Modulus reduction and damping curves are commonly used as input parameters in site response analyses. Until very recently, constitutive models for nonlinear site response analysis could not accurately capture both the desired modulus reduction and damping behavior due to flaws in the assumed functional form for the backbone curve, and/or flaws in the unload-reload relationship. New relationships that solve these issues have recently been proposed, however, they are restricted to defining the in-plane stress-strain relationship for a single plane of shear, and are therefore only appropriate for 1D site response. This paper presents a multi-axial generalization of one of these 1D models, enabling its use in 2D and 3D site response. The 1D model is first briefly described, followed by the multi-axial generalization in terms of stress and strain invariants. The modulus reduction and damping curves are defined in terms of stress ratios rather than shear strains, which allows the model to better capture changes in confining pressure during undrained loading. An example is presented to illustrate fundamental differences in multi-axial versus 1D loading.

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 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: none
Teacher disagreement score0.526
Threshold uncertainty score1.000

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 integrity0.0000.000
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 head0.006
GPT teacher head0.198
Teacher spread0.192 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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