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Record W4396701497 · doi:10.11159/icgre24.110

Analysis of the Influence of Waste Shear Strength Parameters on Landfill Slope Stability

2024· article· en· W4396701497 on OpenAlexvenueno aff
Amila Hasanspahić, Emina Hadžalić, Anis Balić

Bibliographic record

VenueProceedings of the World Congress on Civil, Structural, and Environmental Engineering · 2024
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsnot available
Fundersnot available
KeywordsShear strength (soil)Geotechnical engineeringSlope stabilityStability (learning theory)GeologyEnvironmental scienceComputer scienceSoil science

Abstract

fetched live from OpenAlex

In this paper, the influence of waste shear strength parameters on landfill slope stability is studied.Namely, using the limit equilibrium method in GeoStudio 2018, stability analyses of a typical landfill slope are performed using semi-probabilistic and probabilistic approaches.The semi-probabilistic computations of slope stability are performed based on the available literature recommendations for the waste shear strength parameters.The results obtained for different recommendations are compared and discussed.In the probabilistic computations, the waste shear strength parameters are treated as random variables with Gaussian random distribution, where the parameters of the distribution are again selected based on the literature recommendations.Here, the influence of dispersion in the values of waste shear strength parameters is also examined.In addition, sensitivity analyses are also performed to gain insights into the relative importance of parameters.With the aforementioned analyses, an effort was made to investigate and derive conclusions about how the selection of waste shear strength parameters affects landfill slope stability.

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 score0.178
Threshold uncertainty score0.564

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)0.0000.000

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 head0.005
GPT teacher head0.177
Teacher spread0.172 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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