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Record W4399712232 · doi:10.3208/jgssp.v10.os-8-03

Impact of directly accounting for post-peak strength loss on the dynamic response of a soil column during earthquake loading

2024· article· en· W4399712232 on OpenAlexaff
Tyler J. Oathes, Trevor J. Carey

Bibliographic record

VenueJapanese Geotechnical Society Special Publication · 2024
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsGeotechnical engineeringColumn (typography)GeologyResponse analysisEnvironmental scienceStructural engineeringEngineering

Abstract

fetched live from OpenAlex

This paper presents a numerical investigation of the impacts of post-peak strength loss on ground motion amplification and reduction for a soil column with a layer of strain-softening clay. Stress attenuation and amplification were modeled with a one-dimensional soil column using the finite difference program FLAC 8.1 with the PM4Silt constitutive model. Three calibrations of an idealized soil were developed consisting of different rates and magnitudes of post-peak strength loss. Alternative column geometries were devised, each with different clay layers thicknesses to establish how ground surface motions were affected. Impacts were quantified using both a cumulative ground motion intensity (Arias Intensity) as well as different spectral components (base to surface transfer function). The results illustrate that the thickness of the clay layer surface and strength loss in the layer have an impact on the magnitude and frequency content of the earthquake motion measured at the ground surface. The impact of these results on practice and future research needs are presented.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.393
Threshold uncertainty score0.898

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
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 head0.006
GPT teacher head0.232
Teacher spread0.226 · 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 routes1
Has abstractyes

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