Performance-Based Seismic Design of Hybrid GFRP–Steel Reinforced Concrete Bridge Columns
Bibliographic record
Abstract
Damage quantification in terms of engineering demand parameters (EDPs) is a critical element of the performance-based design (PBD) approach. A widely used EDP for reinforced-concrete (RC) bridge columns is the drift ratio. This study establishes drift ratio limit states, and corresponding strengths for hybrid GFRP–steel RC circular bridge columns. The adopted reinforcement layout in this study consists of two layers of reinforcement, exterior with GFRP and interior with steel. Such coupling between the two materials in concrete bridge columns improves their corrosion resistance while maintaining their stiffness and ductility. Here, a validated fiber-based model is utilized to predict global as well as local responses of hybrid RC columns under monotonic displacement-controlled loading. A full factorial analysis was first adopted to screen parameters potentially influencing drift ratio limit states and corresponding strengths for their significance. The resulting data were then fitted to mathematical expressions using machine learning-based symbolic regression. Lateral load–deformation responses predicted based on the proposed expressions were validated against existing data from the literature. A complete example demonstrating how the proposed expressions could be utilized to design a hybrid bridge column within the context of PBD is also presented.
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How this classification was reachedexpand
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 | 0.000 | 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) | 0.000 | 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 teacher head, 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".