AI-based modeling and data-driven identification of moving load on continuous beams

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.