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Evaluation of shear design criteria of beams according to NBR6118 applying the modified compression field theory

2023· article· en· W4378716747 on OpenAlexaboutno aff
Manoel Victor Rezende Sá, S. H. C. Santos, Júlio Jerônimo Holtz Silva Filho

Bibliographic record

VenueRevista IBRACON de Estruturas e Materiais · 2023
Typearticle
Languageen
FieldEngineering
TopicStructural Behavior of Reinforced Concrete
Canadian institutionsnot available
Fundersnot available
KeywordsStructural engineeringShear (geology)Shear forceBendingBending momentFinite element methodEngineeringMaterials scienceComposite material

Abstract

fetched live from OpenAlex

Abstract It is known that design deficiencies in shear design are more dangerous than bending, as shear failures can occur in a fragile way and without possibility of redistributing internal forces. Unlike bending design, designing for shear loads by different standards can generate significantly different results for the same element, as long as design models have been under discussion for many years. This paper analyses the evaluation of the behavior of the combined bending and shear loads in reinforced concrete beams for different pairs of these forces. For this purpose, the verification presented in ABNT NBR 6118 was used and compared to a more improved theory currently used, the Modified Compression Field Theory - MCFT. This theory is able to predict the relationships of specific loads and strains, as well as the shear strength of sections with great precision, being parameterized by several tested elements. As the use of this theory is not practical for manual calculations, the Response-2000 software, developed at the University of Toronto by Evan C. Bentz, was used. The program allows the analysis of beams and columns subject to moments, shear forces and axial loads, for any type of geometry, material properties and reinforcement arrangement, resulting in accurate responses of the behavior of the sections using MCFT as a basis.

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)0.002
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.074
Threshold uncertainty score0.679

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.050
GPT teacher head0.312
Teacher spread0.262 · 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 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".

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Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueRevista IBRACON de Estruturas e MateriaisSame topicStructural Behavior of Reinforced ConcreteFrench-language works237,207