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Record W4416567510 · doi:10.1016/j.compgeo.2025.107741

Numerical modeling of liquefiable slopes using Hypoplasticity with ISA and semifluidized states: Benchmarking against LEAP centrifuge data

2025· article· en· W4416567510 on OpenAlexaboutno aff
H. Abdellatif, Merita Tafili, J. Duque, David Maš́ın, Torsten Wichtmann

Bibliographic record

VenueComputers and Geotechnics · 2025
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsnot available
FundersDeutsche ForschungsgemeinschaftMinisterstvo Školství, Mládeže a TělovýchovyEuropean Cooperation in Science and Technology
KeywordsCentrifugeConstitutive equationFinite element methodNumerical modelingLiquefactionBenchmark (surveying)Test dataComputer simulation

Abstract

fetched live from OpenAlex

This study analyzes numerical simulations of a water-saturated, liquefiable sandy slope centrifuge test subjected to dynamic base excitation. The benchmark centrifuge experiment was executed within the scope of the LEAP-2017 project by Carey et al., (2020) . Numerical simulations were conducted in PLAXIS, a finite element modeling software, with the soil’s mechanical behavior modeled through a recently developed hypoplastic model for sands, accounting for semifluidized states and fabric change effects, as proposed by Tafili et al., 2024. The constitutive model was carefully calibrated using element tests performed with Ottawa F-65 sand, which was also employed in the centrifuge test. The dataset comprised oedometric compression tests, drained and undrained monotonic triaxial tests, and undrained cyclic triaxial tests, considering different initial densities and effective stresses. A comparison of measured and predicted results revealed reasonable agreement in terms of acceleration time history, spectral response, and the evolution of excess pore water pressure. These findings provide a comprehensive assessment of the predictive capabilities and limitations of the constitutive model in forecasting the behavior of geotechnical structures under earthquake loading.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.759
Threshold uncertainty score0.842

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 head0.013
GPT teacher head0.209
Teacher spread0.196 · 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

Citations3
Published2025
Admission routes1
Has abstractyes

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