Comparative Assessment of CFQST vs Traditional Columns under Lateral Loads
Bibliographic record
Abstract
The study presents a comparative computational analysis of proposed Concrete-Filled Quadruple Steel Tubular (CFQST) columns, concentrating on their lateral load-bearing capacity, lateral ductility, and concrete durability which are essential factors for evaluating seismic risk management.Validated finite element simulations with varying concrete strengths (M25, M40, M50) and slenderness ratios (7, 14, 20) reveal that CFQST columns outperform Reinforced Concrete (RCC), Concrete-Filled Steel Tubular (CFST), and Concrete-Filled Double Steel Tubular (CFDST) columns.CFQST columns demonstrate up to 2.37 times greater lateral load capacity and 1.75 times higher ductility than RCC.When compared to CFST and CFDST columns, CFQST columns offer 1.11 to 1.68 times greater ductility and 1.16 to 1.54 times higher lateral load capacity.The improved performance is due to the confinement effects of four internal steel tubes encased in concrete and confined by an outer steel tube, enhancing the durability of the encased concrete and delaying crushing, resulting in higher energy absorption.The observed "elephant foot" ductile buckling confirms the columns' effectiveness in mitigating the brittle behaviour of concrete.These findings establish CFQST columns as a resilient, durable, and sustainable solution, particularly well-suited for seismic regions and high-risk environments, with significant potential for reducing long-term life cycle costs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".