Reliability Analysis of Bridges for Autonomous Truck Platoons
Bibliographic record
Abstract
Autonomous truck platoon (ATP) deployment on road networks has recently attracted significant interest for its potential economic and environmental benefits. However, the impact of platooning on bridges is a concern because of the differences in their live load characteristics compared with those in the existing bridge design specifications. One of the primary aspects in the safe deployment of ATP is to evaluate the reliability of bridge designed using the existing provisions for live loads from potential configurations of ATP. An analysis procedure is proposed and demonstrated for a simple span steel composite bridge designed according to the existing design provisions. Given that many characteristics of the live load distribution such as the bias factor, coefficient of variation (CoV), and the dynamic amplification factor are presently not known for ATP, a parametric approach is used. The bias factor, dynamic amplification, and CoV are parametrized to calculate the live load distribution and quantify its impact on the reliability index. A two-truck platoon with different headway spacings constituted by different trucks in a single lane scenario is considered. The results indicate that the two single lane bridges designed according to existing design specifications are generally reliable (i.e., achieved the target reliability for which the bridge was initially designed) for the range of ATP live loads investigated when the CoV is less than 0.07, bias close to one and headway distances are above 17 ft. Future studies are suggested to include bridges with multiple spans, other bridge types, and a larger number of trucks in the platoons. The main contribution of this paper is to quantify the reliability indices of selected steel composite bridges designed using the existing specifications but subjected to various configurations of ATP loads and the influence of different components of the live load model attributed to the latter.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".