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Record W4388705393 · doi:10.1680/jgein.23.00070

Evaluating stability of rigid-column-supported and geosynthetic-reinforced embankments

2023· article· en· W4388705393 on OpenAlexaff
Hongyang Liu, Qiang Luo, M. Hesham El Naggar, Kaiwen Liu, Tengfei Wang

Bibliographic record

VenueGeosynthetics International · 2023
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsWestern University
Fundersnot available
KeywordsGeosyntheticsCentrifugeGeotechnical engineeringBending momentStructural engineeringFactor of safetySlip (aerodynamics)GeologyEngineering

Abstract

fetched live from OpenAlex

Field observations and centrifuge tests indicate that progressive column bending failure accompanies most instabilities of rigid-column-supported and geosynthetic-reinforced (RCGR) embankments. However, recognized guidelines specifically for evaluating the overall stability of such systems featuring bending failure remain limited. A general methodology to calculate the factor of safety (FS) for RCGR embankments using the limit equilibrium method is presented in this article. The focus is on deep-seated slope failures, wherein rigid columns progressively fracture due to subsoil overstressing. The concentric arches model, along with tensioned geosynthetic analysis, informs the determination of vertical and horizontal loads on the column heads. The column's resisting moment stems from its flexural and compression resistance. A mobilization factor for the net thrust on each column is defined to capture the progressive failure. The methodology involves an iterative computational procedure to identify the critical slip surface and the FS using the Fellenius' method. The solution was validated using three case studies, including both centrifuge models and field tests, as well as finite-element analysis. The results indicate that the soil mass contributes the most to resisting sliding and overall stability, followed by columns and geosynthetics. In addition, axial force mainly provides the resisting moment of columns.

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 score0.054
Threshold uncertainty score0.600

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)0.0000.000

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 head0.033
GPT teacher head0.287
Teacher spread0.253 · 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

Citations10
Published2023
Admission routes1
Has abstractyes

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