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Record W2029071109 · doi:10.1680/macr.11.00102

Finite-element modelling of S<b>F</b>RC members in bending

2013· article· en· W2029071109 on OpenAlexaff
Renaud de Montaignac, Bruno Massicotte, Jean‐Philippe Charron

Bibliographic record

VenueMagazine of Concrete Research · 2013
Typearticle
Languageen
FieldEngineering
TopicStructural Load-Bearing Analysis
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsStructural engineeringFinite element methodBendingCrackingContext (archaeology)StiffnessThree point flexural testReinforcementUltimate tensile strengthMaterials scienceEngineeringComposite materialGeology

Abstract

fetched live from OpenAlex

This paper is aimed at understanding the mechanics of steel-fibre-reinforced concrete (SFRC) in the context of designing for structural applications. It focuses on the testing procedures adopted to obtain the tensile response of SFRC that are used in finite-element models of structural elements submitted to bending. Modelling of standardised material test specimens enabled validating the assumptions used in inverse analysis to determine the post-cracking σ–w response from bending tests on notched beams and round panels. The effect of fibre orientation, the testing procedure and the validity of standardised test are discussed. Modelling of SFRC structural beams of different scales, shapes, with and without conventional reinforcement, emphasises the importance of using non-uniform material properties within the model to correctly predict the member stiffness and strength, and the crack opening evolution. The paper confirmed that the integration point spacing must be used as the reference length for converting σ–w post-cracking response to σ−ε material properties for carrying out finite-element analysis. Moreover this approach is not affected by the element size and member depth.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.153
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.062
GPT teacher head0.302
Teacher spread0.240 · 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.

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

Citations4
Published2013
Admission routes1
Has abstractyes

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