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Total Stress Analysis of Soft Clay Ground Response in Centrifuge Models

2019· article· en· W2962285286 on OpenAlexaff
Kamil Bekir Afacan, Samuel Yniesta, Ali Shafiee, Jonathan P. Stewart, Scott J. Brandenberg

Bibliographic record

VenueJournal of Geotechnical and Geoenvironmental Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsPolytechnique Montréal
FundersAustralian Government
KeywordsCentrifugeGeotechnical engineeringShear (geology)Shear stressGeologyStiffnessShear modulusMechanicsMaterials scienceStructural engineeringEngineeringPhysicsComposite material

Abstract

fetched live from OpenAlex

This paper presents one-dimensional ground response simulations of centrifuge models involving soft clay deposits subjected to ground motions of varying intensity. Total stress ground response simulations were performed using equivalent-linear (EL) and nonlinear (NL) methods. Shear strains higher than 10% were mobilized during large ground motions; therefore, undrained shear strength of the clay is an important parameter for the simulations. Testing shows that the San Francisco Bay Mud materials used in centrifuge modeling have monotonic shear strengths that increase by 13% per log cycle of shear strain rate. A comparison of simulation results to observations reveals the importance of incorporating shear strength into the development of stress-strain backbone curves, with appropriate consideration of rate adjustments to shear strength and stiffness. NL ground response simulations provide a good match to observed pseudospectral accelerations only when rate-adjusted shear strengths are properly accounted for; otherwise, the NL simulations have significant underprediction bias at oscillator periods less than the soil column period. EL modeling, even with the incorporation of shear strength, leads to unrealistic spectral shapes and overprediction at short spectral periods for tests involving large-strain site response.

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: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.886

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
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 head0.003
GPT teacher head0.164
Teacher spread0.161 · 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

Citations20
Published2019
Admission routes1
Has abstractyes

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