A Systematic Framework for the Seismic Risk Management of RC Bridges Using Hybrid Retrofit Strategies
Bibliographic record
Abstract
Reinforced concrete (RC) bridges are vital components of the transportation infrastructure.However, several existing bridges may fail to meet the target performance objectives of current seismic design standards.Past earthquakes have underscored the vulnerability of substandard bridges to damage modes such as pounding and curvature ductility demands, emphasizing the critical need for seismic retrofitting.This paper proposes a systematic methodology for selecting individual and hybrid retrofit strategies for the seismic risk management of substandard RC bridges.This framework is applied to a benchmark multi-span RC bridge representing a large bridge inventory in a medium seismicity study region.The proposed framework selects retrofit measures that mitigate various damage modes, including excessive bearing displacement, pounding between nearby bridges, and significant curvature ductility demands in bridge bents.The adopted retrofit approaches are verified against previous experimental results through detailed three-dimensional fiber-based modeling and implemented in the representative bridge to evaluate the dynamic response behavior of the existing and retrofitted bridges.The nearby bridges' lateral capacity is initially investigated to assess the retrofit options by analyzing various local damage indicators.Fragility analyses are then performed under different seismic scenarios to compare the effectiveness of the proposed retrofit strategies.Finally, a versatile seismic performance-cost indicator is employed to prioritize the retrofit alternatives and to propose a hybrid retrofit strategy that effectively addresses various damage modes inherent in the substandard bridge and ensures the safety and serviceability of existing RC bridges.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".