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Record W4225154582 · doi:10.11159/icgre22.138

Pseudo-Static Analytical Model For The Static And Seismic Stability Of Dry Stone Retaining Walls

2022· article· en· W4225154582 on OpenAlexvenueno aff
Nathanaël Savalle, Éric Vincens, Paulo B. Lourénço

Bibliographic record

VenueProceedings of the World Congress on Civil, Structural, and Environmental Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsnot available
FundersFundação para a Ciência e a TecnologiaMinistério da Ciência, Tecnologia e Ensino SuperiorEuropean Commission
KeywordsLongitudinal static stabilityStability (learning theory)Static analysisGeotechnical engineeringGeologyComputer scienceStructural engineeringEngineeringAerospace engineering

Abstract

fetched live from OpenAlex

Dry stone retaining walls are made of rubble stones assembled without mortar. They have shaped many regions worldwide and nowadays constitute a high cultural value, aside from their original operational role (agricultural terraces or transportation networks). However, given their high number and old age, they currently need maintenance, whereas only a few tools are available to assess their stability. In particular, the literature lacks a design tool that accounts for seismic loads. In this paper, a pseudo-static approach based on Coulomb's wedge theory is described in detail. General effects of cohesion, water pore pressure and geometrical features of the backfill are considered. On the other hand, the specificities of dry stone retaining walls are introduced through new parameters, whose values are extracted from laboratory and in-situ experiments.

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 score0.005
Threshold uncertainty score0.656

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 head0.009
GPT teacher head0.194
Teacher spread0.184 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

Explore more

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