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.

利用结构健康监测数据，本文提出了一种基于深度学习的长跨度桥梁超载车辆识别方法（DOVI），通过时间卷积架构提取输入序列数据的空间和时间特征，从而提供了一种端到端的超载车辆识别解决方案，无需预先影响线、也不需要提前获得速度和轴距信息，可在多车辆的情况下应用，并在随机交通流条件下，通过对简支梁和长跨度斜拉桥进行模型评估，结果表明，所提出的深度学习超载车辆识别方法具有更好的效果和鲁棒性，相比其他机器学习和深度学习方法。