Vehicle Detection and Classification by Voiceprint Recognition Based on Single Acoustic Sensor Under Bridge Expansion Joint

Vehicle detection and classification (VDC) is a crucial aspect of bridge engineering, as vehicles exert significant dynamic loads on bridges. Various contact and noncontact methods have been proposed for VDC, but finding a durable and cost-effective sensing approach remains challenging for practical bridge applications. In this study, we propose a voiceprint recognition (VPR) method for VDC using a single microphone, offering flexibility and affordability. The distinctive acoustic signatures generated by vehicles impacting bridge expansion joints (BEJs) are captured and utilized for VDC. We optimize a threshold-based algorithm for vehicle detection and a deep learning-based VPR model for vehicle classification. In addition, cascading VPR models enable fine-grained vehicle classification, including lanes and axle types. We validate the proposed method on an actual bridge. The short-term energy (STE) thresholding algorithm achieves a detection accuracy and recall of 90.0% and 91.6%, respectively. The ConFormer model achieves an area under the curve (AuC) of 0.925 for vehicle classification. These results highlight the method as an easy-to-implement and cost-effective solution for efficient VDC surveys. Future work can focus on expanding datasets and incorporating multiple microphones to further enhance the system’s capabilities.