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Record W4366963748 · doi:10.1051/e3sconf/202338213008

Stability analysis of unsaturated soil slope considering softening and non-softening approach

2023· article· en· W4366963748 on OpenAlexaff
Xiuhan Yang, Sai K. Vanapalli

Bibliographic record

VenueE3S Web of Conferences · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicLandslides and related hazards
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsSofteningGeotechnical engineeringSoil waterGeologyInfiltration (HVAC)Strength reductionSlope stabilityFinite element methodMaterials scienceSoil scienceComposite materialStructural engineeringEngineering

Abstract

fetched live from OpenAlex

In conventional geotechnical engineering practice, peak shear strength parameters are widely used in the design of geo-structures constructed with or within unsaturated soils. However, a reduction inshear strength from the peak to the residual value is typically observed during the shear deformation in typical fine-grained unsaturated soils. Several geo-structures in unsaturated soils undergo a large shear deformation prior to reaching the failure condition. Thus, the factor of safety of such geo-structures will gradually but significantly decrease with the development of the shear deformation. For this reason, the strain-softening behaviour of unsaturated soils should be considered for reliable design of the geo-structuresin unsaturated soils. In this study, an unsaturated clay slope under 10 years rainfall infiltration were modelled using softening and non-softening approach using commercial finite element software SIGMA/W. The responses of the studied slope to the long-term rainfall infiltration were analysed. The results of the softening and non-softening analysis were compared. This study provides valuable information with respect to the significance of the strain-softening that are useful in the rational design of slopes in unsaturated soils.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.303
Threshold uncertainty score0.592

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.021
GPT teacher head0.230
Teacher spread0.208 · 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 designObservational
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

Citations1
Published2023
Admission routes1
Has abstractyes

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