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Record W4415163388 · doi:10.1139/cjce-2024-0581

Cyclic performance of CFST column-to-steel beam joints with a novel grooved end-plate connection

2025· article· en· W4415163388 on OpenAlexvenueno aff
Zongyan Li, Zhiqiang Guo

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2025
Typearticle
Languageen
FieldEngineering
TopicStructural Load-Bearing Analysis
Canadian institutionsnot available
FundersNational Natural Science Foundation of ChinaInner Mongolia University of Science and Technology
KeywordsWeldingJoint (building)Ductility (Earth science)StiffnessFinite element methodParametric statisticsTearingConnection (principal bundle)HysteresisBeam (structure)

Abstract

fetched live from OpenAlex

End-plate connections are widely used in steel structures for their semi-rigid behavior, yet conventional designs suffer from end-plate warping, bolt slippage, and limited stiffness. This study proposes an innovative grooved end-plate (GEP) joint for concrete-filled steel tubular column-to-H-beam connections. Three GEP joints and one conventional end plate (OEP) joint were tested under cyclic loading and analyzed via finite element modeling. Results reveal that GEP joints increase rotational stiffness by 29.7% and ultimate bearing capacity by 6.9% compared to OEP joint, failure modes shift from end-plate warping in OEPs to controlled weld tearing in GEPs. Hysteresis curves exhibit spindle-shaped loops without pinching, confirming stable energy dissipation. Parametric studies identify an optimal side extension length of half the column width, balancing stiffness gains with acceptable ductility loss. This work advances semi-rigid joint design by integrating geometric innovation with seismic resilience, providing practical guidelines for high-rise and modular steel structures.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.453
Threshold uncertainty score0.978

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.005
GPT teacher head0.168
Teacher spread0.163 · 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 designSimulation or modeling
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
Published2025
Admission routes1
Has abstractyes

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