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Record W4410875974 · doi:10.1016/j.soildyn.2025.109541

Seismic behaviour of U-shaped retaining walls in non-liquefiable and liquefiable soils

2025· article· en· W4410875974 on OpenAlexafffund
Mokhtar A. Khalifa, Kyungtae Kim, M. Hesham El Naggar

Bibliographic record

VenueSoil Dynamics and Earthquake Engineering · 2025
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGeotechnical engineeringGeologyLiquefactionSoil waterSoil science

Abstract

fetched live from OpenAlex

This paper investigates the seismic performance of U-shaped retaining walls under seismic loading in liquefiable and non-liquefiable soils. Two-dimensional nonlinear finite element models are developed in PLAXIS 2D software. The behaviour of the dry sand soil in the model is simulated using the Hardening Soil Model with Small Stiffness (HSsmall) material model, which can represent the nonlinear behaviour and increased stiffness at small strains of soils and is validated against experimental data. Meanwhile, the UBC3D-PLM material model simulates the liquefiable sand behaviour under seismic loading. The numerical model is employed to investigate the effect of relevant design parameters on the retaining wall seismic response. The effects of wall flexibility, soil strength, stiffness, earthquake frequency content, and peak ground acceleration on the seismic response of the U-shaped retaining wall and both liquefiable and non-liquefiable sandy soils are investigated. Key findings indicate that wall flexibility and soil conditions strongly influence seismic wall deflections and earth pressures, with liquefiable soils showing more notable changes in seismic lateral pressures. Additionally, low-frequency seismic motions significantly impact seismic pressures, mainly through variations in energy dissipation.

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 score0.080
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 head0.003
GPT teacher head0.182
Teacher spread0.179 · 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
Published2025
Admission routes2
Has abstractyes

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