Seismic shear strength of hollow-core composite bridge columns
Bibliographic record
Abstract
The shear behaviour of hollow-core composite short bridge columns under nonlinear static analysis is presented in this study. The investigated composite columns comprised a concrete wall between two concentric circular tubes, an outer circular fibre-reinforced polymer (FRP), and an inner steel tube to form hollow-core FRP-concrete-steel (HC-FCS) columns. Solid three-dimensional numerical models were developed and validated against experimental results. The models subsequently were used to conduct a parametric finite element study by performing a nonlinear static analysis on the shear behaviour of the HC-FCS short columns under combined axial and lateral loadings. The investigated parameters are the effects of the column aspect ratio, steel tube width-to-thickness ratio, confinement ratio, concrete wall thickness, applied axial load level, and column concrete strength. This study revealed that the shear behaviour of HC-FCS short columns is close to an extent to the behaviours of reinforced concrete columns. A comparison between the attained shear strengths and the existing analytical models in the literature was carried out. A critical assessment of these shear strength models revealed vast differences in predicted responses. Finally, a new expression is proposed to predict the shear strength of HC-FCS columns under seismic loads for design purposes and real-life 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) | 0.001 | 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".