A data-driven approach for quantifying reliability and resilience of transportation network
Bibliographic record
Abstract
Bridges are critical transportation infrastructure components, serving as vital links for mobility and evacuation during and after hazard occurrences. Ensuring their reliability and safety is of paramount importance. Traditional reliability assessment methods for bridge networks rely on complex mathematical models, often resulting in time-consuming processes. This paper leverages the power of data-driven algorithms to streamline and enhance the reliability and resilience evaluation of this infrastructure. A novel data-driven method of approach is developed to quantify the resilience of a transportation network within a community to facilitate proactive maintenance and decision-making processes. An ensemble of machine learning algorithms is used to train the predictive model and test its prediction accuracy to mitigate the risk of overfitting. Feature engineering techniques are employed to extract relevant information from the dataset, further enhancing the model’s performance. Subsequently, the estimated reliability indices of bridges are used to perform network analysis and quantify the resilience of the network and its components by accounting for dependency between network components and the degree of centrality of each component for the mobility of the network. The proposed method is illustrated using real-world data for bridges and transportation infrastructure from a US community. The results show that the proposed approach possesses several features. Firstly, its capability to identify vulnerabilities in the transportation network helps inform decisions about prioritizing maintenance efforts during the life cycle of bridges (i.e., prior to extreme hazards) and prioritizing recovery following damaging events. Secondly, its flexibility to adapt to changing environmental conditions and evolving structural characteristics underscores its potential to be integrated into existing bridge management systems, providing a valuable tool for infrastructure stakeholders to make data-driven decisions and allocate resources efficiently.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".