Hybrid retrofitting for upgrading the seismic performance of adjacent bridges vulnerable to different damage modes including pounding
Bibliographic record
Abstract
Earthquake-induced damage, such as pounding, has emphasized the vulnerability of existing bridges, underscoring the essential requirement for retrofitting. This study thus aims to select hybrid retrofit strategies for upgrading the seismic performance of reinforced concrete substandard bridges vulnerable to various damage modes to ensure the safety and serviceability of existing bridges. The study proposes implementing steel dampers to reduce bearing displacement (BD), innovative rubber bumpers in separation gaps to alleviate structural pounding between adjacent bridges, and ultra-high-performance concrete (UHPC) jackets and self-centering buckling restrained braces (SC-BRB) to mitigate curvature ductility (CD) demands of bridge substructure. The idealization of the adopted mitigation measures is verified by comparing their fiber-based numerical models with the hysteretic behavior observed from previous experimental studies. The numerically validated retrofit strategies are then applied to existing benchmark adjacent bridges commonly prevailing in a medium seismicity study area. The adjacent bridges’ dynamic characteristics and lateral capacity are initially investigated to evaluate the retrofit options by monitoring various local damage indices. Fragility analyses are conducted under different seismic scenarios to assess the relative performance of the adopted retrofit schemes and to evaluate the likelihood of exceeding the seismic capacity of the retrofitted bridge structure. Although the probabilistic assessment study indicated that the UHPC and SC-BRB retrofit options enhanced the bridges’ lateral capacity by a comparable ratio of 53 %, the adopted seismic performance-cost indicator of SC-BRB retrofit measure exceeded that of the UHPC alternative by 39 %-79 %, confirming its preference among the considered two alternatives. The steel dampers effectively reduce BD demands, whereas rubber bumpers adequately decrease pounding force demands. This study thus offers insights into the effects of integrating the adopted seismic risk mitigation techniques into an effective and economical hybrid retrofit measure to mitigate the vulnerability of substandard adjacent bridges, thereby ensuring their functionality post-earthquake. • Individual and hybrid retrofit measures are evaluated to upgrade adjacent RC bridges. • Assessment framework is adopted to mitigate seismic demands and enhance performance. • A hybrid mitigation strategy is introduced based on performance and cost indicator. • Integrating individual retrofit techniques ensures substandard bridges’ functionality.
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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".