Deep Learning Overloaded Vehicle Identification for Long Span Bridges
  Based on Structural Health Monitoring Data
Yuqin Li Jun Liu Shengliang Zhong Licheng Zhou Shoubin Dong Zejia Liu Liqun Tang
Overloaded vehicles bring great harm to transportation infrastructures. BWIM
(bridge weigh-in-motion) method for overloaded vehicle identification is
getting more popular because it can be implemented without interruption to the
traffic. However, its application is still limited because its effectiveness
largely depends on professional knowledge and extra information, and is
susceptible to occurrence of multiple vehicles. In this paper, a deep learning
based overloaded vehicle identification approach (DOVI) is proposed, with the
purpose of overloaded vehicle identification for long-span bridges by the use
of structural health monitoring data. The proposed DOVI model uses temporal
convolutional architectures to extract the spatial and temporal features of the
input sequence data, thus provides an end-to-end overloaded vehicle
identification solution which neither needs the influence line nor needs to
obtain velocity and wheelbase information in advance and can be applied under
the occurrence of multiple vehicles. Model evaluations are conducted on a
simply supported beam and a long-span cable-stayed bridge under random traffic
flow. Results demonstrate that the proposed deep-learning overloaded vehicle
identification approach has better effectiveness and robustness, compared with
other machine learning and deep learning approaches.