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Record W4401810524 · doi:10.1680/jgein.24.00070

Complementary use of analytical equations and numerical models for composite liner designs

2024· article· en· W4401810524 on OpenAlexaffabout
Yan Yu, W. Hu, R. Kerry Rowe

Bibliographic record

VenueGeosynthetics International · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicLandfill Environmental Impact Studies
Canadian institutionsQueen's University
Fundersnot available
KeywordsMonte Carlo methodGeomembraneComposite numberLimitingLeakage (economics)Flexibility (engineering)Finite element methodGeosyntheticsRange (aeronautics)Computer scienceStructural engineeringApplied mathematicsEngineeringMathematicsMaterials scienceMechanical engineeringGeotechnical engineeringAlgorithmComposite materialStatistics

Abstract

fetched live from OpenAlex

The finite element (FE) method is used to identify limitations and extend the applicability of an analytical solution for leakage through holed wrinkles in composite liners. The limiting assumption in the analytical model is relaxed based on FE results, providing a means of obtaining good agreement through the two methods over a wide spectrum of cases. Despite the flexibility of the FE, the analytical model has the advantage of readily accommodating a wide range of dimensions and material properties that, even with a very large mesh, may be difficult to analyze using FE. In addition, the analytical equation is well-suited for Monte Carlo simulation. This is illustrated by the use of the modified analytical equation and the Monte Carlo simulations to compare the performance of two types of composite liners currently approved for use in Chinese landfills with a generic design in Canada and other parts of the world. It is shown that even with a relatively small number of holed wrinkles leakage through the geomembrane composite liner can differ by up to two orders of magnitude between composite liners considered to be equivalent. The paper also notes the need to revise regulations to reflect the evolution of knowledge.

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 score0.922
Threshold uncertainty score0.911

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)0.0010.000

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 head0.096
GPT teacher head0.316
Teacher spread0.221 · 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

Citations5
Published2024
Admission routes2
Has abstractyes

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