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Seismic Behaviour of Synthetic-Frc Columns

2017· article· en· W2766672019 on OpenAlexaff
Patrick Paultre, Rami Eid

Bibliographic record

VenueIOP Conference Series Materials Science and Engineering · 2017
Typearticle
Languageen
FieldEngineering
TopicStructural Behavior of Reinforced Concrete
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsDuctility (Earth science)ReinforcementMaterials scienceDissipationStructural engineeringCompressive strengthComposite materialTransverse planeReinforced concreteEngineering

Abstract

fetched live from OpenAlex

Inclusion of short discrete fibres into the concrete mixture can increase the compressive strength and ductility of normal-strength concrete (NSC) and high-strength concrete (HSC) column specimens under compressive loading as already has been shown by several studies. Concrete design codes ensure ductile behaviour of columns by setting a requirement for a minimum amount of transverse steel reinforcement. Therefore, the inclusion of discrete short fibres into the concrete mixture combined with a reduced amount of lateral reinforcement can be an alternative to the latter full amount required by the codes. This paper presents tests that were performed on large-scale fibre-reinforced NSC circular columns under cyclic flexure and constant axial load simulating earthquake loading. The aim of this test program is to examine the combined confinement effect of steel or synthetic fibres and the transverse steel reinforcement type (spirals or hoops) on the structural performance of RC columns. The results show that in terms of ductility and energy dissipation, the behaviour of the fibre-reinforced concrete (FRC) specimens is improved compared to the nonfibrous ones. This behaviour is also predicted by the proposed confinement model which takes into account the mechanical and the geometrical properties of the concrete and the reinforcement as well as those of the fibres.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.818

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 studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.014
GPT teacher head0.220
Teacher spread0.206 · 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".

Quick stats

Citations1
Published2017
Admission routes1
Has abstractyes

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