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Record W4399712427 · doi:10.3208/jgssp.v10.os-10-02

Impacts of ground motion characteristics on liquefaction triggering and lateral displacement of a sloping ground

2024· article· en· W4399712427 on OpenAlexafffund
Masoumeh Asgarpoor, Mahdi Taiebat

Bibliographic record

VenueJapanese Geotechnical Society Special Publication · 2024
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsLiquefactionGeotechnical engineeringGround motionGeologyDisplacement (psychology)Soil liquefactionSeismology

Abstract

fetched live from OpenAlex

Subduction motions typically exhibit lower amplitudes, longer durations, and lower frequencies compared to crustal motions. This paper initiates the assessment of how these ground motion characteristics impact liquefaction-induced responses in sloping ground by presenting a numerical study that isolates these effects. In an initial endeavor to dissect and understand the effects of these variables, single-frequency sinusoidal ramp waves with varying amplitudes, durations, and frequencies are used in this study for fully coupled nonlinear dynamic analysis of a mildly-sloping liquefiable soil column. The analyses are carried out in OpenSees using the SANISAND-MSf v2 soil constitutive model. The simulation results indicate that increasing the maximum amplitude of the base excitation leads to a decrease in the number of cycles required to trigger liquefaction, while simultaneously increasing the end-of-motion surface lateral displacement. Increasing the number of cycles with the maximum amplitude would not affect the pre-liquefaction response if the triggering happens in the early loading cycles, but can increase the surface lateral displacements as the soil remains in the post-liquefaction stage for a longer duration. Both pre- and post-liquefaction soil responses under various motion frequencies can exhibit distinct patterns depending on the intensity of the motion and the natural frequency of the soil column.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.628
Threshold uncertainty score0.802

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 head0.008
GPT teacher head0.225
Teacher spread0.217 · 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

Citations1
Published2024
Admission routes2
Has abstractyes

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