Vehicle Classification System Using In-Pavement Fiber Bragg Grating Sensors

Vehicle classification is critical due to its significant use in transportation and pavement management and maintenance. In this paper, a vehicle classification system is developed based on in-pavement 3-D glass fiber-reinforced polymer packaged fiber Bragg grating sensors (3-D GFRP-FBG). When vehicles pass by the pavement, it produces strains, which can be monitored by the center wavelength changes of the embedded 3-D GFRP-FBG sensors. The vehicle's speed and wheelbase can then be estimated according to the different time a vehicle arrived at the sensor sites and speeds monitored from the wavelength changes of the in-pavement sensors. The vehicle classification system in this paper uses support vector machine learning algorithms to classify vehicles into categories ranging from small vehicles to combination trucks. The field testing results from real traffic show that the developed system can accurately estimate the vehicle classifications with 98.5% of accuracy.