Numerical analysis of buckling behaviour of timber-encased steel composite columns under axial compression
Bibliographic record
Abstract
Timber (e.g. Douglas-fir, spruce-pine-fir)-encased steel composite (TESC) columns provide a feasible application for timber in large-scale and high-rise structures. However, the load-carrying mechanism and buckling behaviour of TESC columns have not been thoroughly studied. This paper investigates the axial load distribution and buckling behaviour of TESC columns with embedded H-section steel through finite element (FE) analysis. FE models were built and validated by the experimental results. A parametric study was then conducted to assess the structural responses and load distribution of TESC columns with different geometric and physical parameters, including the steel area, timber area, slenderness ratio, steel yield strength and timber compressive strength parallel to grain. The numerical results revealed that increasing the slenderness ratio could enhance the confinement effect of the timber in improving the maximum load. A larger proportion of timber area provided a more significant confinement effect in enhancing the ductility of the steel, and then a minimum area ratio for TESC columns considering the confinement effect of timber was determined. Finally, the numerical results produced by 360 FE models of TESC columns were employed to evaluate the buckling curves of four current codes, and two new buckling curves for TESC columns were also proposed.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".