Torsion and Combined Torsion-Axial Load Behaviour of Concrete Filled Steel Tube Columns with and without ECC/CFRP Wrap
Bibliographic record
Abstract
Concrete-filled tube columns may experience coupled torsion and axial compression under seismic and wind loading. Fifteen square, rectangular, and circular concrete filled steel/aluminium tube (CFST/CFAT) are tested under pure torsion to understand the behaviour and develop/validate finite element (FE) models. Parametric studies under pure torsion are conducted using FE models to study the effect of geometric (tube cross-section, length, and thickness) and tube/concrete material parameters on torsional strength, stiffness, and tube-concrete composite interaction. FE models are used to study strength, stiffness, and failure modes of CFST columns under combined torsion and axial compression load at various preload levels of axial and torsion. Coupled torsional preloading and subsequent axial load reduce the axial load capacity significantly, while coupled axial preloading and subsequent torsional loading have shown no significant influence on the torsional strength of CFST columns. Implementation of FE models developed for CFST columns with engineered cementitious composite (ECC) and carbon fibre reinforced polymer (CFRP) wraps showed significant enhancement of axial strength and stiffness compared to control (without wrap) even under high torsional preload level. The energy absorption capacity and ductility of the CFRP wrapped CFST column are about 1.38 and 1.1 times higher than its counterpart with ECC wrap of same axial load capacity. Overall, the results suggest that use of both CFRP and ECC wrap is an effective approach to enhance the torsional behaviour and ductility of CFST columns under combined torsion and axial loading conditions. However, high crack resistance and better post-peak ductility of ECC wrapped CFST compared to sudden CFRP rupture failure in CFRP-wrapped counterpart should be taken into consideration for seismic applications.
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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) | 0.002 | 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".