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Record W4399712067 · doi:10.3208/jgssp.v10.ss-1-04

Seismic response of liquefiable backfill supported by sheet-pile with different wall embedment ratios

2024· article· en· W4399712067 on OpenAlexafffund
Sheng Zeng, Andrés Reyes, Mahdi Taiebat

Bibliographic record

VenueJapanese Geotechnical Society Special Publication · 2024
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsEmbedmentGeotechnical engineeringSheet pileGeologyPileLiquefaction

Abstract

fetched live from OpenAlex

Analysis of the seismic design of retaining structures is complex due to the intricate interplay between the response of backfill soil and supporting wall. When dealing with liquefiable soils, numerical modeling is often employed to gain insight into the mechanisms behind the resulting deformation of retaining walls during earthquakes. This paper focuses on detailed numerical modeling of two well-documented centrifuge tests of such systems from the last two rounds of LEAP, with different embedment ratios and shaking intensities, and their impacts on the system response of the sheet-pile wall supporting a liquefiable submerged deposit. First, a soil constitutive model is calibrated using data from cyclic direct simple shear tests. The two centrifuge models with different wall embedment ratios and shaking intensities are then simulated and used for validation and assessment purposes. The numerical model shows a successful performance in capturing the system response for both models. Assessing details of the stress-strain response in the numerical model reveals two dominant cyclic deformation mechanisms in the backfill soil: cyclic mobility and the accumulation of residual deformation. The success of the adopted numerical approach in capturing the experimental results is attributed to the constitutive model's ability to simulate both of these cyclic deformation mechanisms.

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)0.000
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 score0.162
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.006
GPT teacher head0.202
Teacher spread0.197 · 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

Citations0
Published2024
Admission routes2
Has abstractyes

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