Towards a low-damage seismic design: Developing and validating a performance-based design framework for segmental post-tensioned precast concrete piers
Bibliographic record
Abstract
Segmental post-tensioned precast concrete (SPPC) piers show significant potential for enhancing post-earthquake rehabilitation, yet a practical performance-based seismic design framework remains undeveloped. In this respect, the current study develops and validates a comprehensive framework, demonstrating SPPC piers’ ability to minimize damage to individual pier components while maintaining the integrity of the overall bridge system. The framework employs a two-tier design methodology that integrates a capacity-demand-diagram approach with fragility analysis. The capacity-demand-diagram method is used to ensure that the displacement demands of the SPPC piers are consistent with predefined seismic displacement targets at the design earthquake level, thus facilitating the efficient determination of preliminary design parameters. Subsequently, fragility analysis is employed to assess the probability of failure, allowing iterative refinement of the design parameters to meet damage tolerance requirements. The developed framework is validated through a case study that compares SPPC piers with conventional piers in high-seismic regions. This step-by-step analysis confirms the applicability of the framework and shows that SPPC piers can be effectively integrated into current seismic design practices, supporting a performance-based framework with ease and reliability.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".