Damage Identification Based on the Loaded Frequency Changes and Deep Learning for Bridge

Abstract

Abstract Frequency is widely used in damage identification as the dynamic characteristic parameter with the simplest measurement method and the highest measurement accuracy, but the frequency changes are nonunique parameters of damage location for the symmetrical structure. To improve the method of damage location identification based on frequency changes for bridge, the bridge damage identification network based on loaded frequency changes and deep learning theory is proposed by introducing the moving vehicle detection method. A continuous beam example is used for testing, and the test results show that the identification network based on the bridge load frequency changes and the Stacked Denoising Auto-Encoder(SDAE) network can effectively locate the damage of bridge, which enables the frequency changes data to overcome the nonunique limitation of the symmetrical structure, enhances the expression ability of the parameters and improves the effectiveness and the anti-noise property of bridge damage location identification.