Numerical analysis of buckling behaviour of timber-encased steel composite columns under axial compression
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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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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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) | 0.000 | 0.000 |
Machine scores (provisional)
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Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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