Application of Loaded Frequency and Deep Learning in Bridge Damage Identification
As a dynamic characteristic parameter with simple measurement method and high accuracy,frequency has been widely used in the field of damage identification,but the existing frequency variation parameters have the problem of non-uniqueness.Therefore,a bridge damage identification network based on the on-load frequency index and deep learning theory is proposed,and the damage identification index parameters are constructed to identify the damage state of the bridge by using the on-load frequency data before and after the damage.The results show that the identification network can effectively locate the damage of bridge structure,and the frequency data overcomes the non-uniqueness limitation of the frequency change of symmetrical structure,enhances the expression ability of parameters,and improves the effectiveness and anti-noise performance of bridge damage location.
万方数据
维普
