High‐Resolution Regional Seismic Loss Assessment of Reinforced Concrete Bridges Using Component‐Level Fragility Models and Repair Cost Estimations
Bibliographic record
Abstract
ABSTRACT This study develops a high‐resolution regional seismic loss assessment framework for reinforced concrete (RC) bridges, focusing on direct losses due to bridge repair and replacement. Indirect losses tied to traffic downtime, business disruption, delayed recovery, etc., can also be attributed to bridge damage but are considered outside the current scope. While previous studies have made relevant attempts on regional bridge seismic loss assessment, most relied on limited hazard simulations, simplified fragility models, and generic repair cost ratios. In contrast, the current study bears its novel contribution to conduct high‐resolution assessment that directly aggregates loss contributions from individual bridge components. Using 1152 RC bridges in the City of Los Angeles as a case study, the framework integrates crucial steps that (1) generate numerous seismic intensity maps, (2) classify the inventory into 26 bridge groups, (3) assign each bridge group with second‐generation seismic fragility models, and (4) develop a stochastic loss function for each bridge through component‐level cost estimations. The high‐resolution assessment enables new insights for more in‐depth loss disaggregation analysis across varying return periods, individual bridges, bridge components, and repair actions. Research findings for LA bridges indicate that early‐designed, multi‐span bridges contribute disproportionately to the overall regional losses. The framework also supports detailed sensitivity analysis toward explicit loss uncertainty quantification. Overall, the proposed high‐resolution assessment framework enhances the fidelity, interpretability, and actionability of regional loss results, offering a transferable and scalable methodology for more effective seismic loss mitigation and post‐earthquake recovery planning.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".