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Record W6980696518

Comparison of geosynthetic reinforced soil wall solutions using analytical design methods and numerical modelling

2023· article· en· W6980696518 on OpenAlexaboutno aff

Bibliographic record

VenueUPCommons institutional repository (Universitat Politècnica de Catalunya) · 2023
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsnot available
Fundersnot available
KeywordsServiceability (structure)StiffnessFinite element methodUltimate tensile strengthReinforcementNumerical analysisLimit state designMechanically stabilized earthNumerical models
DOInot available

Abstract

fetched live from OpenAlex

European design standard (prEN-1997 202x) permits the use of suitably verified numerical models for the design of reinforced soil walls (RSW). The paper first compares three analytical design method outcomes (Coherent Gravity, Simplified, and Stiffness method) available in US (AASHTO 2020) and Canadian (CSA 2019) design codes. Polyester (PET) strap reinforcement arrangements from each method were then simulated using a 2D finite element model (FEM) which considered construction stages and transient compaction conditions. The models for each arrangement were analyzed using different material factors applied to the soil frictional strength. Serviceability limit states (SLS) and ultimate limit states (ULS) were evaluating via horizontal wall deformations, soil shear strains, and maximum reinforcement tensile loads. Numerical results remained within SLS and ULS criteria. The maximum tensile loads from the numerical models were close to, but lower than the predicted loads using the Stiffness Method, which in turn were lower than the loads predicted using the Simplified and Coherent Gravity methods.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0070.001

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 head0.072
GPT teacher head0.310
Teacher spread0.238 · 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 source (direct Gemma or distilled Codex), 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
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueUPCommons institutional repository (Universitat Politècnica de Catalunya)Same topicGeotechnical Engineering and Soil StabilizationFrench-language works237,207