Full-Field Dynamic Displacement Reconstruction of Bridge Based on Modal Learning

Abstract

Full-field dynamic displacement (FFDD) is important for bridge condition assessment. However, it is challenging to monitor the FFDD with high accuracy due to limited sensors and environment variation. This paper proposes a FFDD reconstruction method for bridge based on modal learning. Firstly, the transfer function of dynamic strain response of finite points (SRFP) and FFDD are derived based on the beam bending theory and modal superposition method. Then iterative particle swarm optimization (IPSO) is employed to facilitate self-learning of mode shape with the ability of adapting environment variation. Subsequently, the procedure for reconstructing bridge FFDD by utilizing SRFP and the learned transfer function is provided. Finally, the effectiveness of the proposed method is verified by numerical and experimental examples of bridge under random load, impact load, and moving load excitation, and effects of sensor placement, road roughness, and measurement noise on the reconstruction accuracy are systematically investigated. The results indicate that the proposed method can accurately reconstruct the FFDD in the presence of environment variation, road roughness, and measurement noise at the cost of limited sensors.