Performance-Based Seismic Design for Retrofitting Deficient Bridge Bents: Developing Performance-Based Damage States
Bibliographic record
Abstract
The performance-based seismic design (PBSD) approach is implemented to achieve the desired structural performance over a wide range of seismic hazard levels. It requires a set of targeted performance levels and their corresponding limits to be defined. Because the current codes and guidelines do not prescribe these limits for different performance levels for old bridges with seismic deficiencies, such as inadequate ductility and low shear strength, this study aims to develop them. In this study, quantitative damage states that are expressed as drifts and damage indices (DIs) at various performance levels are developed using incremental dynamic analyses for retrofitted bents. Four retrofit options: (1) steel; (2) carbon–fiber-reinforced polymer (CFRP); (3) concrete; and (4) engineered cementitious composite (ECC) jackets are considered in this study. The concrete and longitudinal reinforcement of all bents cracked and yielded at limiting drifts of 0.06% and 0.38%, respectively. In addition, the ECC-jacketed bent experienced core crushing of the concrete at the highest limiting drift of 4.16%. In addition, a detailed example complements this study, which presents how retrofitting could be designed by considering the target seismic performance that uses the proposed damage states. The first-mode spectral accelerations of the bents were the optimum intensity measures (IMs) to study their relative performance for noncumulative and cumulative damage measures (DMs) at various hazard levels. Drift is considered noncumulative, and the DI that includes the combined effect of maximum drift and absorbed hysteretic energy is considered cumulative. The steel jacket was the most effective when decreasing the median maximum drift of the retrofitted bent, and the ECC jacket reduced the median DI of this type of bent the most.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".