Hurricane vulnerability of coastal bridges using multiple environmental parameters
Bibliographic record
Abstract
Hurricanes and other severe storms have proven themselves to be one of the major threats to transportation assets throughout the world, particularly to bridges located along coastal areas.Bridges as key components of transportation networks have shown to be vulnerable to hurricane-induced wave and surge forces.A large number of bridges along the U.S. Gulf coast suffered severe damage from recent hurricanes.Current risk-assessment practices include the fragility analysis of bridges based on a single hazard intensity parameter such as peak ground acceleration.However, this study investigates the vulnerability of highway bridges against hurricanes for multiple hazard parameters, not including the risk of substructure failure due to scour and/or erosion.The proposed hurricane vulnerability assessment methodology is applied to bridges along the surge-prone coastal regions of the state of Georgia.The surge-prone region is identified by the USGS SLOSH maps, and vulnerable bridges are selected based on the available NBI database.Nonlinear bridge models have been developed to apply a time history of wave loading as a function of the wind speed and storm water depth.Different combinations of bridge geometric and material parameters are generated to develop meta-models which cover a wide range of bridge configurations and wave/surge loads.This study yields a fragility function which describes the probability of failure for vulnerable bridges in terms of two environmental parameters: wind speed and storm water depth.The findings of this study will ultimately be beneficial to policy makers prioritizing recovery efforts and allocation of essential resources.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".