Structural damage identification method based on local generalized curvature modal information entropy and BP neural network

Abstract

Abstract: In view of the fact that curvature mode is less sensitive to vibration mode nodes and can not estimate damage quantitatively, a formula of mode information entropy for generalized local curvature is deduced, based on generalized local information entropy and by introducing conception of the curvature model. The corresponding damage index is established therefore. A simple supported beam is analyzed by using the finite element software Midas civil. The dynamic parameters of the simply supported beam are extracted and processed. The first-order curvature mode and the local generalized curvature modal information entropy are used respectively as two input parameters for the neural network to identify the damage. The conclusion of two parameters having been identified are compared with each other so as to study the influence of the number of measure points on the identification accuracy of each index. The results show that the generalized local curvature mode information entropy as the input parameter of the neural network can locate and quantify the damage much better. The accuracy of indicators near the vibration mode node is higher than the result by the method of the curvature mode. When the number of measuring points takes 33, the identification accuracy becomes the highest.

Key words: generalized local curvature modal information entropy, curvature modal, BP neural network, damage location, damage quantification

CLC Number:

TU312
U447

XIANG Chang-sheng, YUAN Zi, ZHOU Yu. Structural damage identification method based on local generalized curvature modal information entropy and BP neural network[J]. Journal of Lanzhou University of Technology, 2021, 47(1): 129-135.

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