Research on hierarchical identification of structural damage based on dynamic characteristics by deep belief networks
To identify structural damage efficiently and accurately,a hierarchical identification method of structural damage based on structural dynamic characteristics and deep belief network is proposed by combining machine learning with intelligent algorithm,and the damage position and degree are identified in turn.In order to identify the damage location,a 6-element vector is established by using the first three vertical vibration frequencies of the structure and the third modal displacement of a single node,and the damage location is identified by using the 6-element vector as input parameters.To identify the damage degree,the first three natural frequencies and modal displacements of vertical vibration or the 6 nodes modal curvature differences are used as parameters to input the depth confidence network to identify the damage degree.A simple-supported beam is taken as a model to verify it.It is shown that the damage position recognition accuracy can reach 100%even if the noise level reaches 10%.When identifying the damage degree,the deep belief network based on six-node modal curvature difference has strong noise resistance.The maximum relative error of damage degree prediction is less than 5.08%and the mean square error is 0.4878 under the noise of 15%.Compared with BP neural network,the prediction ability of BP neural network is better than that of deep belief network when there is no noise.Under the same noise level,the prediction ability of depth belief network is obviously better than that of BP neural network,which shows that the hierarchical identification method of structural damage based on structural dynamic characteristics and deep belief networks has strong robustness and high accuracy of identification results.
deep belief networks(DBN)damage identificationnoise immunityBP neural network
NETL
NSTL
万方数据
维普
