Scenario-based earthquake damage assessment of highway bridge networks
Bibliographic record
Abstract
Abstract In earthquake-prone regions, the evaluation of seismic impacts on bridges is crucial to mitigation, emergency, and recovery planning for highway networks. The degree of bridge damage determines the cost and time required for repairs and the level of post-earthquake functionality including disruption of transportation network, increased costs due to reduction of traffic flow and restricted access to emergency routes. The article presents the methodological development and implementation of an interactive web application for rapid geospatial assessment and visualisation of earthquake damage scenarios of municipal highway bridge networks based on open access datasets. The proposed framework consists of the following successive models: hazard, inventory, damage, and impact. The seismic hazard model generates spatial distribution of the shaking intensity for earthquake scenarios in terms of ground motion intensity measure using ground motion prediction equations based on seismic hazard model for Eastern Canada. The shaking intensities are then modified with local site amplification factors based on the Canadian highway bridge design code values. The inventory model provides a database of existing bridges based on open-access data which are then classified according to their seismic vulnerability. The damage model assesses seismic performance of classes of bridges by applying respective fragility functions represented as probabilistic relationships between the intensity measure and the degree of expected damage. The impact model evaluates the post-earthquake traffic-carrying capacity of the highway network based on the predicted damage including repair cost as a percentage of replacement cost of bridges and inspection priority. The web-application is demonstrated with a bridge network in Quebec City including 117 bridges subjected to 180 earthquake scenarios. The proposed methodology is particularly useful to facilitate direct communication of potential impacts to emergency managers and city transport officials.
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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 | 0.001 |
| 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".