Automated Truck Platooning–Bridge Interaction: Assessing Dynamic Impacts on Drilled Shaft Foundations
Bibliographic record
Abstract
This study aims to investigate the impact of automated truck platooning on bridge safety and serviceability, with a particular focus on the dynamic effects on bridge substructures. Automated truck platooning allows multiple trucks to travel in close proximity at high speeds, reducing aerodynamic drag and fuel consumption. However, concerns arise about the increased load effects on bridges, necessitating a thorough examination of their safety implications. Previous studies have mainly focused on the static load capacity of bridge superstructures under platoon traffic, identifying the inadequacy of existing design standards; this study expands on that by examining the dynamic impacts of truck platoons on bridge substructures. It specifically assesses the risk of pile foundation settlement under varied platoon configurations and operational parameters, such as driving velocity, number of trucks, and headway spacing. The methodology incorporates vehicle–bridge interaction simulations for dynamic load analysis, soil–structure interaction modeling for load–displacement characterization, and reliability assessments to determine the service limit state of pile shafts. Ultimately, the analysis results seek to inform the development of recommendations for truck platooning safety regulations and bridge management to ensure the safe implementation of platooning technology.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".