Truck Platoon Impacts on Prestressed Concrete Girder Bridges
Bibliographic record
Abstract
‘Connected and Autonomous Vehicle’ technology allows for the formation of truck platoons which consists of closely spaced trucks travelling at high speeds. Although platooning offers many benefits, its impact on bridges and current design codes require attention. Specifically, because platoons may be heavier than what bridges were designed to withstand and because the Canadian Highway Bridge Design Code (CHBDC) in CSA S6:19, which offers empirical equations to estimate the live load distribution factor (Ft), may not be adequate for autonomous vehicles, especially regarding lane width requirements. The goal of this study is twofold: first, assess the performance of prestressed concrete girder bridges under truck platoons, considering corrosion and climate change effects, and second, evaluate the performance of CHBDC equations in estimating Ft for bridges with reduced lane widths that do not meet standard requirements and provide an amplification factor for improved accuracy. This study begins by comparing the reliability of bridge archetypes under routine traffic versus truck platoon loads. We then incorporate structural deterioration models to account for the effects of corrosion and climate change. Finite element models are employed to evaluate CHBDC Ft estimates, and genetic programming is utilized to develop the amplification factor. The results indicate that closely spaced platoons, corrosion and climate change all decrease the bridge reliability and that even if platooning satisfies the safety criteria at the ultimate limit state, it may falter at the serviceability limit state. CHBDC performs well for interior moment Ft estimates, but has unconservative cases for exterior moment and shear, especially for bridges with reduced lane widths. The load type showed negligible effect on Ft, while reduced lane widths consistently increased Ft. The proposed amplification factor was validated and resulted in more accurate Ft estimations.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".