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Finite-Element Modeling of Partially Encased Composite Columns Using the Dynamic Explicit Method

2007· article· en· W2110419135 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Structural Engineering · 2007
Typearticle
Languageen
FieldEngineering
TopicStructural Behavior of Reinforced Concrete
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFinite element methodStructural engineeringFlangeColumn (typography)BucklingComposite numberDeformation (meteorology)Failure mode and effects analysisMaterials scienceEngineeringComposite material

Abstract

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Prediction of the behavior of partially encased composite (PEC) columns by finite-element modeling presents a challenging problem due to local buckling of the thin steel flange plates and rapid volumetric expansion of the concrete near the ultimate load. The use of a dynamic explicit formulation and a concrete damage plasticity model has permitted good predictions of the capacities of both uniaxially and eccentrically loaded PEC column tests reported in the literature. The model provides good representations of the axial deformation at the peak load, the postpeak behavior, and the failure mode observed in those tests. The finite-element model is also capable of predicting the effect of different link spacings on the behavior of the PEC columns as well as determining the individual contributions of the steel and concrete to the total load carrying capacity of these columns. In addition to a description of the methodology, a discussion of the results of finite-element studies of PEC columns from three experimental programs encompassing a wide variety of geometries and loading conditions is presented in this paper. A study is also presented on the effects of local imperfections on the capacity of these columns.

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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 score0.360
Threshold uncertainty score0.886

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.018
GPT teacher head0.274
Teacher spread0.256 · 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