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Record W4251600491 · doi:10.32920/ryerson.14644923.v1

Analytical modeling of the shear behavior of reinfored concrete exterior beam-column joints

2021· preprint· en· W4251600491 on OpenAlexaff
Reza Sadjadi

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicStructural Behavior of Reinforced Concrete
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsStructural engineeringJoint (building)Shear (geology)Beam (structure)ReinforcementNonlinear systemReinforced concreteColumn (typography)Finite element methodGeologyMaterials scienceEngineeringComposite materialConnection (principal bundle)Physics

Abstract

fetched live from OpenAlex

In reinforced concrete structures, failure of beam-column joint was observed as one of the major causes of damage of those structures during earthquakes. Non-ductile detailing of reinforcement in the joint in terms of inadequate shear reinforcement in the joint panel or insufficient anchorage of the beam bars within the joint region are the main causes of deficiency in the performance of joints during an earthquake. The objectives of this study are to compare different aspects of modeling the nonlinear behavior of exterior beam-column joints and also to propose a new model for the shear behavior of exterior joints. Two well-known computer programs for nonlinear dynamic analysis of the structures DRAIN-2DX (element type 15) and IDARC2D are used. The advantages of using each one for the beam-column joints in the reinforced concrete structures, and the effect of modeling features on the response of beam-column joints are discussed.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0010.000
Research integrity0.0010.000
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 head0.023
GPT teacher head0.247
Teacher spread0.224 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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
Published2021
Admission routes1
Has abstractyes

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