Seismic performance assessment and design of concrete bridge piers reinforced with hybrid glass fiber reinforced polymer & steel rebars
Bibliographic record
Abstract
In highly seismic regions, design requirements of transverse reinforcement in bridge piers result in sections with highly congested reinforcement, especially over plastic hinge regions. To avoid reinforcement congestion and improve concrete quality, this study explored a combination of new reinforcement arrangements and materials in Reinforced Concrete (RC) bridge piers. This study explored double-confined RC bridge piers with alternative reinforcement details, including large-scale double-confined steel (DCS) and double-confined hybrid (DCH) columns, using a blend of Glass Fibre-Reinforced Polymer (GFRP) and steel reinforcement. The proposed design consists of longitudinal rebars in two layers: an external GFRP cage and an internal steel cage, each transversely reinforced with their respective materials. This approach merges GFRP's corrosion resistance with steel's ductility, thereby improving the performance of bridge piers in seismic regions. Comprehensive testing of standard, DCS, and DCH piers under cyclic loading revealed differences in load-bearing capacities, damage progression, and ductility. DCS piers exhibited superior ductility, while GFRP, within DCH, enhanced corrosion resistance without compromising stiffness. Subsequently, the study shifted its focus towards employing FRP wraps to restore bridge piers affected by seismic activity. The effectiveness of these FRP wraps was analyzed by comparing the (As-built) condition to those after repair, including both Control and DCH piers. It included retesting procedures and results, highlighting the repaired specimens' enhanced performance in resisting higher loads and dissipating more energy at later testing stages, demonstrating improved ductility and energy dissipation after repair. The study progressed to implementing Performance-Based Design (PBD) procedures for bridge columns, analyzing the seismic performance and PBD damage states of conventional, DCS, and DCH piers. In this comparison, DCS and DCH demonstrated promise as substitutes for conventional RC columns. A fibre-based mode accurately predicting pier responses under cyclic loads was developed as part of the analytical work. A series of moment-curvature analyses were also performed to develop pier design aids in the form of design charts correlating effective stiffness with applied axial load. The factorial analysis identified key parameters affecting drift ratios and strengths. Machine learning techniques were utilized to derive mathematical expressions that accurately predicted load-deformation responses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".