Unsupervised Vehicle Classification Using a Structural Health Monitoring System

Abstract

The structural performance assessment of bridges is a crucial issue for managing transportation infrastructure systems in EU countries as traffic loads and structural ageing continues to increase. Weight-in-Motion (WiM) systems have been developed to estimate the gross weight of vehicles over a bridge and keep the bridge load under control. However, WiM systems are costly in procurement and installation; alternative approaches that aim to be more scalable and cost-effective are needed to respond to the need to monitor large-scale infrastructures. This work explores an innovative zeroincremental cost approach based on raw vibration data extracted from a system already deployed for Structural Health Monitoring (SHM) and based on MEMS accelerometers. A novel signal processing and classification pipeline has been developed to differentiate vehicles into three categories: light, i.e., less than 10 tons; heavy, i.e., between 10 and 30 tons; and super heavy, i.e., above 30 tons, using only features extracted from vibration data. The results show that this framework can distinguish vehicles with an accuracy of 96.87%, utilizing the mean-shift unsupervised clustering model. This method has the potential to be a significantly cost-effective and scalable solution for monitoring bridge loads compared to WiM systems, as it leverages existing SHM infrastructure and affordable MEMS sensors to provide real-time information on vehicular loads.