Department of Civil and Environmental EngineeringNorth Dakota State University, Fargo, ND, 58108-6050, U.S.ADepartment of Civil and Environmental EngineeringNorth Dakota State University, Fargo, ND, 58108-6050, U.S.A firstname.lastname@example.org
Mu'ath Al-Tarawneh;Ying Huang
The vehicle classification system developed by Federal Highway Administration (FHWA) of United States divides vehicle type into 13 categories depending on the number of axles and the wheelbase. However, establishing a fixed threshold for classifying a vehicle is difficult. The overlapping between vehicles pattern in the system needs a pattern recognition technique to distinguish between different vehicle categories. In this study, machine learning algorithms were used to classify various vehicles based on the collected traffic data from the embedded three-dimension Glass Fiber-Reinforced Polymer packaged Fiber Bragg Grating sensors (3D GFRP-FBG). The investigated machine learning algorithms include the support vector machines (SVM), Neural Network, and k-nearest neighbors (KNN) algorithms.