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Record W2886371048 · doi:10.1139/cgj-2018-0071

Evaluation of the accuracy of design methods for geosynthetic-reinforced piled embankments

2018· article· en· W2886371048 on OpenAlexvenueno aff
Ewerton C. A. Fonseca, Ennio M. Palmeira

Bibliographic record

VenueCanadian Geotechnical Journal · 2018
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsnot available
FundersMinistério da EducaçãoConselho Nacional de Desenvolvimento Científico e Tecnológico
KeywordsGeotechnical engineeringPileStructural engineeringReinforcementSettlement (finance)LeveeGeosyntheticsEngineeringComputer science

Abstract

fetched live from OpenAlex

Geosynthetic-reinforced piled embankments have been increasingly used as a solution to stabilize embankments on soft subgrades. Analytical methods have been commonly used in routine designs of such works and significant deviations between results can occur depending on the method considered. This paper investigates the accuracy of different analytical methods by comparing their predictions with results of large-scale tests. A section of piled embankment consisting of an instrumented fill layer subjected to varying values of surface surcharge was simulated in the laboratory. Four different reinforcement types were tested. Results of the tests are presented and discussed, and statistical analyses were carried out to assess the accuracy of each method with regard to pile efficacy, maximum fill settlement, and reinforcement strain. Results show that the identification of the most accurate and precise method depends on the statistical criterion used. However, in general, the methods based on the concentric arches theory, the modified British method (BS8006), and the German method (EBGEO) were the ones that showed the best predictive capability.

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)0.003
metaresearch head score (Gemma)0.004
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 score0.973
Threshold uncertainty score0.449

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
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)0.0000.000

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 head0.044
GPT teacher head0.312
Teacher spread0.268 · 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

Citations24
Published2018
Admission routes1
Has abstractyes

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