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Shear Strength of Hybrid Fiber Reinforced Self-Compacting Concrete Beams

2023· article· en· W4390050102 on OpenAlexaff
Isabela Ereno da Silva, Isabela de Gois Laufer, Gustavo Savaris, Rodnny Jesus Mendoza Fakhye, Carlos Eduardo Tino Balestra, Ana Bergmann

Bibliographic record

VenueSemina Ciências Exatas e Tecnológicas · 2023
Typearticle
Languageen
FieldEngineering
TopicInnovative concrete reinforcement materials
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsCrackingMaterials scienceUltimate tensile strengthSynthetic fiberComposite materialShear (geology)Residual strengthShear strength (soil)FiberStructural engineeringEngineeringGeology

Abstract

fetched live from OpenAlex

Self-compacting concrete has constructive advantages over conventional concrete, such as reducing labor and construction time, mainly because of its fluidity in the fresh state. However, in the hardened state, it maintains low performance when tensioned, and the fibers can be added to the mixture, maintaining a portion of the resistance after cracking. Steel fibers are usually added to concrete, but recently synthetic fibers have been used, due to their lower cost and non-corrosive nature, but with lower tensile strength. Thus, by combining the two types of fibers, the benefits of each material can be used. This work presents the results of an experimental program to evaluate the effect of the hybridization of metallic and synthetic fibers on the shear strength of self-compacting concrete beams without stirrups. The results demonstrate that both steel and hybrid fibers result in greater shear strength compared with the reference concrete without fibers before shear crack formation; however, the greatest advantages are attributed to post-cracking residual strength. The experimental results were compared with estimates calculated using equations published in the literature, demonstrating the feasibility of using some existing equations for concretes with the addition of hybrid fibers.

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), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.120
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.015
GPT teacher head0.241
Teacher spread0.226 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2023
Admission routes1
Has abstractyes

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