Research on Bridge Structural Damage Identification

Abstract

The traditional identification methods have limited ability to identify damage location of bridge structures. Therefore, a bridge structural damage location identification method based on deep learning is proposed. In addition, the sigmoid function is the activation function, and the cross entropy is the cost function. Meanwhile, take the Gaussian noise as the addition method and take the softmax as the classifier. So the constructed SDAE deep learning model can realize damage location identification of the simply supported the continuous beam bridges. Compared with the traditional identification methods of bridge structures, namely BP network and SVM, the proposed method shows higher identification accuracy and antinoise performance. Here, the average identification accuracy of the method for continuous beam bridge is 99.8%. As can be seen that the proposed method is more suitable for practical bridge structure damage location identification.

Experimental Study on Bridge Structural Damage Identification Based on Quasi-Static Displacement Effects and Wavelet Packet Decomposition

“…This subtraction yields the QSDIL damage index, which is used for the preliminary assessment of bridge structure damage and the rough localization of the damaged area. (6) In step (5), after approximately localizing the damaged area, an increased number of sensors should be strategically placed in that region. These additional sensors are used to measure the dynamic response signals of acceleration time history at various points within the initially identified damage location.…”

“…(3) identification methods based on static characteristics; and (4) identification methods based on time-frequency signals [1][2][3][4][5]. Dynamic fingerprints, indicative of structural dynamic characteristics, involve discerning the structural damage state by observing changes in dynamic fingerprints before and after damage occurrence [6,7].…”