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Record W3202639194 · doi:10.1680/jgeen.21.00125

Strength parameter selection framework for evaluating the design life of clay cut slopes

2021· article· en· W3202639194 on OpenAlexaff
H. Postill, P. Helm, Neil Dixon, Ashraf El‐Hamalawi, S. G. Glendinning, W. Andy Take

Bibliographic record

VenueProceedings of the Institution of Civil Engineers - Geotechnical Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Analysis
Canadian institutionsQueen's University
FundersEngineering and Physical Sciences Research Council
KeywordsGeotechnical engineeringStrength reductionSofteningResidualResidual strengthStructural engineeringLimit analysisStrength of materialsGeologyEngineeringMathematicsMaterials scienceFinite element methodComposite material

Abstract

fetched live from OpenAlex

The design of engineered earthworks is predominantly conducted through limit equilibrium analysis, requiring strain-independent strength criteria. Previous studies on deep-seated first-time failures within over-consolidated clay cut slopes have proposed the use of fully softened strength parameters for design. A study investigating shallow first-time failures in clay cut slopes due to seasonal stress cycles was undertaken using a validated numerical model capable of capturing seasonal ratcheting and progressive failure. It was found that fully softened strength criteria are inappropriate for the assessment of shallow first-time failures due to seasonal ratcheting and slopes at angles between the material's fully softened and residual friction angle may be at risk of failure in the future due to this behaviour. However, adopting residual strength parameters will likely result in overly conservative solutions considering the required design life of geotechnical assets. It was found that the strain-softening behaviour of clay defines the rate of strength deterioration and the operational life of engineered slopes. While general guidelines for analysis considering shallow first-time failures in clay cut slopes are provided, detailed understanding of a material's strain-softening behaviour, the magnitude and rate of strength reduction with strains, is needed to establish strength criteria for limit equilibrium analysis.

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)0.006
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: none
Teacher disagreement score0.863
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.022
GPT teacher head0.248
Teacher spread0.226 · 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

Citations10
Published2021
Admission routes1
Has abstractyes

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