PDF(Pubmed)
Abstract：
Traffic load identification for bridges is of great significance for overloaded vehicle control as well as the structural management and maintenance in bridge engineering. Unlike the conventional load identification methods that always encounter problems of ill-condition and difficulties in identifying multi parameters simultaneously when solving the motion equations inversely, a novel strategy is proposed based on smart sensing combing intelligent algorithm for real-time traffic load monitoring. An array of lead zirconium titanate sensors is applied to capture the dynamic responses of a beam bridge, while the Long Short-Term Memory (LSTM) neural network is employed to establish the mapping relations between the dynamic responses of the bridge and the traffic load through data mining. The results reveal that, with the real-time strain responses fed into the LSTM network, the speed and magnitude of the moving load may be identified simultaneously with high accuracy when compared to the practically applied load. The current method may facilitate highly efficient identification of the time-varying characteristics of moving loads and may provide a useful tool for long-term traffic load monitoring and traffic control for in-service bridges.
摘要：
桥梁交通荷载识别对于车辆超载控制以及桥梁工程的结构管理和维护具有重要意义。与传统的载荷识别方法在逆求解运动方程时总是遇到病态和同时识别多参数困难的问题不同,提出了一种基于智能传感结合智能算法的实时交通负荷监测策略。一系列钛酸铅锆传感器用于捕获梁桥的动态响应，采用长短期记忆(LSTM)神经网络,通过数据挖掘建立桥梁动态响应与交通荷载之间的映射关系。结果表明,通过将实时应变响应馈送到LSTM网络中，例如,当与实际施加的负载相比时,移动负载的速度和大小可以以高精度同时被识别。当前方法可以促进移动负载的时变特性的高效识别,并且可以提供用于服务中的桥梁的长期交通负载监测和交通控制的有用工具。
