Truck Platooning to Minimize Load-Induced Fatigue in Steel Girder Bridges

Braguim, Thales Couto; Lou, Peng; Nassif, Hani

doi:10.1177/0361198120973657

Cited by 8 publications

(5 citation statements)

References 4 publications

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“…Based on the truck platoon load models developed by utilizing a SU4 truck, Sayed et al [ 25 ] found that the bridge internal forces caused by ATP loads can be more significant compared to those under a single-truck load. For steel bridges, Couto Braguim et al [ 26 ] found that truck platoons with different inter-truck spacings can cause various degrees of fatigue damage to bridges, and taking proper control of the inter-truck spacing can help reduce the fatigue damage.…”

Section: Literature Reviewmentioning

confidence: 99%

Load Effect of Automated Truck Platooning on Highway Bridges and Loading Strategy

Ling

Deng

et al. 2022

Sensors

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Automated truck platooning (ATP) has gained growing attention due to its advantage in reducing fuel consumption and carbon emissions. However, it poses serious challenges to highway bridges due to the load effect of multiple closely spaced heavy-duty trucks on the bridge. In China, ATP also has great application prospects in the massive and ever-increasing highway freight market. Therefore, the load effects of ATP on bridges need to be thoroughly investigated. In this study, typical Chinese highway bridges and trucks were adopted. ATP load models were designed according to the current Chinese road traffic regulations. The load effects of ATP on highway bridges were calculated using the influence line method and evaluated based on the Chinese bridge design specifications. Results show that the load effect of ATP on bridges increases with the increase in the gross vehicle mass and the truck platooning size but decreases with the increasing inter-truck spacing and the critical wheelbase. The Grade-I (best quality standard) highway bridges are generally capable of withstanding the ATP loads, while caution should be exercised for other bridges. Strategies for preventing serious adverse impacts of ATP load on highway bridges are proposed.

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Section: Literature Reviewmentioning

confidence: 99%

Load Effect of Automated Truck Platooning on Highway Bridges and Loading Strategy

Ling

Deng

et al. 2022

Sensors

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“…Previous studies for ATPs on steel bridges are based on deterministic analyses with the FDOT C5 truck ( 11 , 12 , 14 ). In the present study, FHWA class 9, FDOT C5, and New Jersey Turnpike Authority (NJTA) Type 352 ( 22 ) trucks are used to constitute the ATPs.…”

Section: Candidate Trucksmentioning

confidence: 99%

“…Similarly, the load factor ratings (LFRs) were adequate for truck platoons in the positive moment region and less so in the negative moment regions for all span lengths. Couto Braguim et al ( 14 ) studied the possibility of reducing fatigue damage in steel girder bridges by allowing truck platooning. They performed line girder analyses for sets of truck platoons on simple and continuous span bridges.…”

Section: Relevant Literaturementioning

confidence: 99%

Reliability Analysis of Bridges for Autonomous Truck Platoons

Sajid

Chouinard

Légeron

et al. 2022

Transportation Research Record: Journal of the Transportation R

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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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“…The Florida C5, Delaware T540, Alabama 3S2 AL, Kentucky Type 4, Mississippi HS-Short, and AASHTO Type 3S2 were among the six five-axle truck models studied. In 2021, Couto Braguim et al [26] found that truck platooning led to high load effects, but it reduced fatigue damage due to the reduction in the number of stress cycles.…”

Section: Introductionmentioning

confidence: 99%

Advancing infrastructure resilience: machine learning-based prediction of bridges’ rating factors under autonomous truck platoons

Elshazli,

Hussein,

Bhat

et al. 2024

J Infrastruct Preserv Resil

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The operational characteristics of freight shipment will significantly change after the implementation of Autonomous and Connected Trucks (ACT). This change will have a significant impact on freight mobility, transportation safety, and the sustainability of infrastructure. Truck platooning is an emerging truck configuration that is expected to become operational in the future due to the rapid advancements in connected vehicle technology and autonomous driving assistance. The platooning configuration enables trucks to be connected with themselves and the surrounding infrastructure. This arrangement has shown to be a promising solution to improve the vehicles’ fuel efficiency, reduce carbon dioxide emission, reduce traffic congestion, and improve transportation service. However, platooning may accelerate the damage accumulation of pavement and bridge structures due to the formation of multiple load axles within each platoon since those structures were not designed for such loads. According to AASHTO, bridges are designed based on a notional live load model comprised of one or two trucks per lane in conjunction with or separate from an applied uniform load (AASHTO, LRFD 2022). This damage, if accumulated, its repair would require billions of dollars from the government and would impede the movement of both people and goods. The potential damage to infrastructure may arise due to various factors such as the number of trucks in a platoon, gap spacing between trucks, and the type of trucks. This research work includes a thorough parametric study with 295,200 computer simulations using SAP 2000. The goal was to evaluate the effect of different truck platooning configurations on the load rating of existing bridges. The obtained results served as the dataset for training various machine learning models, including Random Tree, Random Forest, Multi-Layer Perceptron (MLP), Support Vector Regression (SVR), K-Nearest Neighbor (KNN), and Extreme Gradient Boosting (XGBoost). Results showed that Random Forest model performed the best, with the lowest prediction errors. The proposed machine learning model has shown its effectiveness in identifying optimal platooning configurations for bridge structures within the scope of the study. Graphical Abstract

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Truck Platooning to Minimize Load-Induced Fatigue in Steel Girder Bridges

Cited by 8 publications

References 4 publications

Load Effect of Automated Truck Platooning on Highway Bridges and Loading Strategy

Load Effect of Automated Truck Platooning on Highway Bridges and Loading Strategy

Reliability Analysis of Bridges for Autonomous Truck Platoons

Advancing infrastructure resilience: machine learning-based prediction of bridges’ rating factors under autonomous truck platoons

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