Dynamic Load Allowance of Bridges Subject to Autonomous Truck Platooning
Bibliographic record
Abstract
This research is aimed at evaluating the effect of influencing parameters on the dynamic load allowance (DLA) for steel composite bridges subject to autonomous truck platooning (ATP). A comprehensive literature review is presented on platooning configurations and their effect on bridges, the selection of candidate trucks to constitute ATP, and DLA analyses. Next, the modeling of trucks, the road surface roughness generation in MATLAB, and its realization in Abaqus® as 3-D finite element modeling is presented in detail. A parametric study of the DLA is performed for a single span steel girder bridge for a single truck and platoons with two or three trucks, with speed ranging from 60 to 100 km/h, inter-truck spacings between 6 and 10 m, and three road surface roughness profiles (ISO 8608 profile A, ISO 8608 profile B, and ISO 8608 profile C). The results indicate that the resonance of the bridge can be excited by truck platoons for specific speed and inter-truck spacings, which can increase the dynamic load allowance relative to that of a single truck. Combinations of platoon speed and spacing that result in resonance conditions and high DLA vary as a function of surface roughness. When resonance conditions are not encountered, and inter-truck spacings are small, such that all platoon trucks are simultaneously on the span, the DLA is smaller compared with a single truck for smooth surface profiles. Large inter-truck spacings for two-truck and three-truck platoons result in high DLA but lower static loads, which can result in dynamic effects that are not within current Canadian Standards Association (CSA) guidelines, especially for large road surface roughness. ATP on a smooth profile result in only marginal increases of the DLA compared with a single truck and are within current CSA S6 guidelines.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".