Leveraging AI and Machine Learning for Bridge Structural Assessment and Connected Trucks Advancement

The operational characteristics of freight shipment will significantly change after the implementation of Autonomous and Connected Trucks (ACT). This change will remarkably impact mobility, safety, and infrastructure service life. Truck platooning is one of the truck arrangements that will become feasible in the near future with the rapid development of connected vehicle technology and automatic driving support. 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. This damage, if accumulated, will cost the government billions of dollars to fix and will affect the mobility of 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. In this research work, 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) were used to identify the optimal truck platooning configurations for bridge structures within the scope of the study. The machine learning models were used to algorithmically provide Google Earth maps with the optimal routing for a specific platooning configuration.