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Record W2999718542 · doi:10.1061/9780784481455.058

Volumetric Strains from Inverse Analysis of Pore Pressure Transducer Arrays in Centrifuge Models

2018· article· en· W2999718542 on OpenAlexaboutno aff
Kathleen M. Darby, Ross W. Boulanger, Jason T. DeJong

Bibliographic record

VenueGeotechnical Earthquake Engineering and Soil Dynamics V · 2018
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsnot available
FundersDivision of Civil, Mechanical and Manufacturing InnovationUniversity of California, DavisNational Science Foundation
KeywordsCentrifugePore water pressureAccelerationGeotechnical engineeringTransducerDissipationLiquefactionRADIUSDrainageMaterials sciencePressure sensorMechanicsGeologyAcousticsPhysics

Abstract

fetched live from OpenAlex

Inverse analyses were used to evaluate the degree of partial drainage occurring during dynamic shaking of liquefying soil profiles in a set of centrifuge model tests. Three tests were performed using the 9-m radius centrifuge at the UC Davis Center for Geotechnical Modeling on saturated Ottawa sand models with initial relative densities of 25, 43, and 80%. Models were subjected to multiple sinusoidal shaking events with acceleration amplitudes ranging from 0.03 to 0.55 g. Densely spaced pore pressure transducer arrays provided profiles of pore pressure generation and dissipation; inverse analyses of the pore pressure data were used to obtain volumetric strain profiles during shaking and dissipation. Surface settlements computed by integrating the volumetric strain profiles are compared to surface settlements measured from linear potentiometers. The magnitude of the volumetric strains due to partial drainage and their potential effects on liquefaction responses are discussed.

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: Empirical
Teacher disagreement score0.280
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.007
GPT teacher head0.181
Teacher spread0.174 · 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

Citations3
Published2018
Admission routes1
Has abstractyes

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