Structural Adequacy and Network Criticality: An Integrated Approach for Prioritizing Bridge Adaptation to Automated Truck Platooning
Bibliographic record
Abstract
The emergence of connected and autonomous vehicles technology presents a unique challenge for existing highway bridges. Trucks forming platoons and traveling in close, high-speed formations offer fuel efficiency gains but also exert increased load effects on existing highway bridges. This study addresses this concern by introducing a risk-based assessment framework that combines evaluations of structural adequacy and network criticality. This integrated approach assesses and prioritizes bridges for necessary rehabilitation, ensuring the readiness of the highway system for platooning. First, at the component level, it evaluates each bridge’s load-bearing capacity and current structural condition, determining its capability against the increased loads characteristic of truck platoons. Second, at the network level, it considers each bridge’s role and importance within the broader transportation network, using network topology metrics to quantify the potential widespread impact of any bridge failure. The developed method was utilized to evaluate the preparedness of highway bridges in Ontario for accommodating truck platooning. The results show that bridges that have transportation criticality generally meet structural requirements for supporting truck platoons. However, overlooking network-level measures might result in biased prioritization of bridges for upgrades. This study supports strategic budgeting for necessary bridge upgrades, which is crucial for safe, efficient platooning.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".