Resilience Quantification of Transportation Infrastructure Subjected to Hazards
Bibliographic record
Abstract
Evaluating the resilience of transportation infrastructures, including bridges, roads, and tunnels, is a critical aspect of ensuring the ongoing functionality and reliability of urban or regional areas in the face of various disruptive events. Such infrastructures are susceptible to a range of disruptions which can have significant impacts on their ability to function effectively. Resilience refers to the capacity of an infrastructure or a system to withstand and recover from these disruptions. This research presents a framework to evaluate the resilience surface for assessing the resilience of various transportation infrastructure components. This comprehensive approach involves several steps. First, the framework identifies unique damage configurations by performing a fragility analysis. This analysis allows for a better understanding of how susceptible the infrastructure is to different hazards. Next, the framework focuses on the restoration of the affected infrastructure by developing recovery curves for each identified damage configuration. This is done by taking into account relevant restoration data and considering the specific characteristics of each configuration. Additionally, the framework acknowledges the inherent uncertainty that exists within various aspects of infrastructure resilience assessment. These uncertainties include hazard intensity, modeling uncertainty, and the restoration process itself. By incorporating these uncertainties into the framework, a more accurate and reliable assessment can be achieved. The utility of this framework is demonstrated through its application to a real-world case study involving a highway bridge located in Canada. The goal of this research is to offer decision-makers a valuable tool for evaluating the resilience of transportation infrastructure. This can contribute to more robust and reliable transportation infrastructures, capable of withstanding and recovering from a wide range of disruptive events.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| 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) | 0.001 | 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".