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Application of a Simplified Anisotropic Constitutive Model for Soft Structured Clay on Embankment Failure

2021· article· en· W3160628164 on OpenAlexaboutno aff
Ali Shirmohammadi, Masoud Hajialilue‐Bonab, Davood Dadras-Ajirloo

Bibliographic record

VenueInternational Journal of Geomechanics · 2021
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsnot available
Fundersnot available
KeywordsGeotechnical engineeringLeveeAnisotropyConstitutive equationDeformation (meteorology)SimplicityGeologyComputer scienceEngineeringStructural engineeringFinite element method

Abstract

fetched live from OpenAlex

The design and maintenance of embankments is still a challenge in practical geotechnical engineering because of some features of soft sensitive soil behavior that are not considered in conventional methods. These features originate from the soil structure, including soil anisotropy, interparticle bonding, and decay as a result of the loading and deformation process. In recent years, many efforts have been made to incorporate the aforementioned features in various soil constitutive models. However, their application in practical geotechnical engineering is limited, owing to the complexity of the models, a number of parameters, and difficulties in the implementation in a computer code. The aim of this study is to modify a simple anisotropic constitutive model (SANICLAY) in order to take into account destructuration, named SANICLAY-D, and its implementation in computer code with a simple and robust algorithm. The capability of the proposed soil model in simulating the effects of the aforementioned soil features on the behavior of the well-known Test Embankment A constructed at Saint-Alban, Quebec, Canada, is explored. This model predicts, with sufficient accuracy, the effect of anisotropy on embankment failure behavior, especially the height and the failure surface, despite its simplicity.

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 score0.974
Threshold uncertainty score0.509

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 head0.009
GPT teacher head0.234
Teacher spread0.225 · 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

Citations8
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of GeomechanicsSame topicGeotechnical Engineering and Soil MechanicsFrench-language works237,207