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Record W3111991786 · doi:10.1016/j.soildyn.2020.106465

Effect of prior small to moderate seismic events on monotonic undrained shear strength of sand

2020· article· en· W3111991786 on OpenAlexaboutno aff
T.-W. Wu, Yusuke Suzuki, Brian Carlton, C. B. Harbitz, Achim Kopf

Bibliographic record

VenueSoil Dynamics and Earthquake Engineering · 2020
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsnot available
FundersEuropean Commission
KeywordsMonotonic functionGeotechnical engineeringGeologyShear (geology)Pore water pressureInstabilityEffective stressShear strength (soil)Seismic loadingOverburden pressureMechanicsMathematicsSoil waterPetrologySoil science

Abstract

fetched live from OpenAlex

In this study we investigate the effect of prior seismic shaking on the monotonic shear strength of saturated Ottawa Sand 20/30. We perform a series of stress-controlled undrained cyclic triaxial tests with different seismic intensities intentionally without causing failure, followed by drainage of the excess pore pressure and an undrained monotonic loading test to determine the undrained shear strength. The experimental data show that small to moderate seismic events that do not fail the specimen can significantly increase undrained shear strength without much change in relative density. One prior seismic event with peak ground acceleration ~ 1.3 m/s2 may increase the undrained shear strength of a specimen at ~ 10 m depth by around 30%. The results also show that as the intensity of the shaking increases, the increase in the monotonic shear strength increases. However, the strengthening effect does not increase with the number of seismic events although a small degree of global densification in the sample is observed. The results of this paper will help assess the change in static slope stability after a single or multiple small to moderate events occurred without causing initial instability.

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 score0.050
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 head0.005
GPT teacher head0.178
Teacher spread0.173 · 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

Citations10
Published2020
Admission routes1
Has abstractyes

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