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Record W4385979043 · doi:10.4203/ccc.3.9.2

Numerical Modelling of Flat Arch Masonry Retaining Walls

2023· article· en· W4385979043 on OpenAlexafffund
Hasini Rathnayake, Ali Ahmed, George Iskander, M.C. Kurukulasuriya, Nigel G. Shrive

Bibliographic record

VenueCivil-comp conferences · 2023
Typearticle
Languageen
FieldEngineering
TopicMasonry and Concrete Structural Analysis
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaCompute CanadaUniversity of Calgary
KeywordsArchStructural engineeringStiffnessFinite element methodAbutmentGeotechnical engineeringMasonryLateral earth pressureDeflection (physics)EngineeringParametric statisticsGeologyMathematics

Abstract

fetched live from OpenAlex

A vast majority of arches can be found as bridges in railway and roadway systems, aqueducts, and roofs.However, employing arch action to resist lateral earth pressure has not been exploited broadly in the literature.A recent study has investigated the potential of utilizing flat arch unreinforced concrete block retaining wall to resist the lateral earth pressure and surcharge loading.The proposed retaining wall was constructed as a segmental circular flat arch.This novel concept was a success, with experimental stresses and deflections well below critical limits.Despite the success, the wall's deflection profile was unexpected, possibly due to loss of fixity at the abutments.Therefore, the authors were unable to numerically replicate their experimental measurements, preventing them from presenting a general response of this structural system.This paper investigates the possibility of explaining the experimental results as a result of abutment slippage.A 3-D finite element simplified micro model, verified using a thick-cylinder analysis, is used to numerically reproduce the experimental setup.The influence of grout stiffness and arch wall-abutment coefficient of friction on the loss of fixity is investigated.A detailed discussion on the influence of these factors and a renewed analysis of the experimental results is presented.The model confirmed the hypothesis of fixity loss as producing the experimental deflected shape.The development of this model makes possible a parametric analysis characterising the response of the structural system.

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 score0.057
Threshold uncertainty score0.637

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 head0.044
GPT teacher head0.235
Teacher spread0.191 · 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

Citations0
Published2023
Admission routes2
Has abstractyes

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