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Record W1998540351 · doi:10.1680/macr.2005.57.4.211

Non-linear model for reinforced concrete under cyclic loading

2005· article· en· W1998540351 on OpenAlexaff
Aly Said, Mostafa Elmorsi, M. Nehdi

Bibliographic record

VenueMagazine of Concrete Research · 2005
Typearticle
Languageen
FieldEngineering
TopicStructural Behavior of Reinforced Concrete
Canadian institutionsWestern University
Fundersnot available
KeywordsStructural engineeringFinite element methodMaterials scienceShear (geology)Bauschinger effectStiffeningHardening (computing)StiffnessReinforced solidReinforced concreteGeotechnical engineeringPlasticityComposite materialEngineering

Abstract

fetched live from OpenAlex

Most of the available shear models for reinforced concrete rely on empirical formulations. In this study, a rational shear stress function is used to define the shear stress–strain envelope for reinforced concrete. Cyclic rules are proposed to define the loading, unloading and reloading relationships for reinforced concrete under shear stress reversals. A normal stress function describing the cyclic relationship of concrete under axial stress is also introduced. The proposed functions are verified using experimental data of reinforced concrete panels tested under monotonic and cyclic loading. Subsequently, the normal and shear stress functions along with their cyclic rules are integrated in a non-linear finite element analysis code. The resulting model accounts for tension stiffening, crack opening and closing, compression hardening and softening, degradation of concrete strength and stiffness in the direction parallel to the crack, compression unloading and reloading, as well as non-linear steel behaviour (strain hardening and Bauschinger effect). The finite element model is then used to analyse two Portland Cement Association shear walls with different geometries tested under cyclic loading. The results show a good agreement between analytical and experimental data. The model showed an excellent capacity of predicting shear deformations of reinforced concrete elements under cyclic loading with minimal computational efforts.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.547
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.058
GPT teacher head0.339
Teacher spread0.280 · 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 designBench or experimental
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

Citations10
Published2005
Admission routes1
Has abstractyes

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