Finite-Element Modeling of Partially Encased Composite Columns Using the Dynamic Explicit Method
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".