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Record W3208298684 · doi:10.1680/jgein.21.00015a

Influence of geosynthetic stiffness on analytical solutions for reinforced fill over void

2021· article· en· W3208298684 on OpenAlexaff
Fahimeh M. Naftchali

Bibliographic record

VenueGeosynthetics International · 2021
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsRoyal Military College of CanadaQueen's University
Fundersnot available
KeywordsStiffnessReinforcementLimit state designServiceability (structure)Structural engineeringUltimate tensile strengthGeosyntheticsGeotechnical engineeringMaterials scienceEngineeringComposite material

Abstract

fetched live from OpenAlex

Analytical solutions for geosynthetic reinforced fills over a void have appeared in the literature starting in the 1980s. Current solutions pay little or no attention to the influence of the creep-reduced stiffness of the geosynthetic reinforcement under tensile loading. This paper addresses this gap by introducing a reinforcement stiffness limit state in the design of these systems. The choice of reinforcement stiffness is based on a simple two-parameter hyperbolic isochronous load–strain model developed by the authors and applied to a large database of uniaxial and biaxial geogrids and woven geotextiles. The paper provides a design chart procedure that can be used with four well-known analytical solutions to compute the maximum reinforcement load. In addition to the stiffness limit state, the design chart approach includes vertical deformation and reinforcement strain serviceability limit states, and a tensile strength limit state. A novel feature of the design charts is a quantitative link to the ultimate strength of the reinforcement to estimate the isochronous stiffness of the reinforcement for different elapsed loading times and strains. There are many instances in the literature where the reinforcement stiffness was taken from a constant rate-of-strain tensile test. The paper shows that this is non-conservative for design.

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)0.001
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 score0.043
Threshold uncertainty score0.558

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.013
GPT teacher head0.237
Teacher spread0.224 · 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

Citations14
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueGeosynthetics InternationalSame topicGeotechnical Engineering and Soil StabilizationFrench-language works237,207