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Record W2786046359 · doi:10.1139/cgj-2017-0609

Numerical analysis of concrete-faced rockfill dams considering effect of face slab – cushion layer interaction

2018· article· en· W2786046359 on OpenAlexaffvenue
Miad Saberi, Charles-Darwin Annan, Jean‐Marie Konrad

Bibliographic record

VenueCanadian Geotechnical Journal · 2018
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsSlabCushionGeotechnical engineeringFinite element methodStructural engineeringStress (linguistics)Materials scienceSurface finishConstitutive equationDisplacement (psychology)Layer (electronics)GeologyEngineeringComposite material

Abstract

fetched live from OpenAlex

In this study, the effect of concrete face slab – cushion layer interface behavior on the performance of face slabs in concrete-faced rockfill dams (CFRDs) is investigated using the finite element method. The body of the CFRD is simulated by the cap elastoplasticity model while the interface zone between the concrete face slab and the gravelly cushion layer is simulated explicitly by an advanced interface constitutive model, developed in the framework of critical state soil mechanics and state parameters, and capable of simulating volumetric behavior and stress path dependency. The effect of elastic and elastoplastic material behavior and water level on the stress and displacement responses of the concrete face slab is examined, and the influence of the roughness at the interface area between the concrete face slab at the contact face and the cushion layer on the performance of the concrete face slab is investigated. Comparison of the results with and without the explicit consideration of the interface behavior shows the importance of incorporating advanced constitutive interface modeling in the design and analysis of CFRDs.

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: Empirical
Teacher disagreement score0.126
Threshold uncertainty score0.878

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.008
GPT teacher head0.235
Teacher spread0.227 · 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

Citations28
Published2018
Admission routes2
Has abstractyes

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