Modeling post‐earthquake functional recovery of bridges
Bibliographic record
Abstract
Rapid restoration of transportation systems following earthquake disruptions is essential due to its significant impact on commutes, freight transport, and emergency medical response. This study develops a framework to model bridge post‐earthquake functional recovery, which is a critical step in assessing the seismic resilience of transportation networks. A simulation‐based strategy is used, which is initiated by component‐level damage assessment. This is followed by three major modules that include functional state evaluation, impeding factor definition and duration, and repair or replacement duration. The functional state module determines the bridge closure decisions (e.g. partial or complete lane closure, weight restriction) immediately after the earthquake and during the reopening phase. The duration estimates include the time delays before the initiation of repairs (in the impeding factor module) and the time needed to perform any necessary repairs or to replace the bridge (in the repair or replacement duration module). Worker allocation schemes and repair sequencing are explicitly considered to realistically reflect local construction practices. The framework architecture and duration‐based input parameters were informed by a series of interviews with California bridge engineers and builders. Nonetheless, the overall methodology is flexible and can be easily adapted to other jurisdictions.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".