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A Two-step Approach for Damage Identification in Axially Functionally Graded Beams
LIU Jike,LV Zhongrong * #
School of Engineering, Sun Yat-sen University, Guangzhou 510006, P.R. China
*Correspondence author
#Submitted by
Subject:
Funding: Guangdong Province Science and Technology Program (No.2014A020218004), National Natural Science Foundation of China (No.11172333, 11272361), Guangdong Province Natural Science Foundation (No.2015A030313126), Doctoral Program Foundation of Ministry of Education of China (No.20130171110039)
Opened online: 5 January 2017
Accepted by: none
Citation: LIU Jike,LV Zhongrong.A Two-step Approach for Damage Identification in Axially Functionally Graded Beams[OL]. [ 5 January 2017] http://en.paper.edu.cn/en_releasepaper/content/4715444
 
 
This study presents a two-stage approach based on residual force vector and response sensitivity analysis for structural damage identification in axially functionally graded (AFG) beams. The local damage is simulated by a reduction in the elemental Young's modulus of the beam. The residual force vector is used to find the possible damaged elements in the beam at first. Then, a hybrid objective function is established and a sensitivity-based model updating method is adopted to identify the perturbation of the stiffness parameter from the measured dynamic responses. Two numerical examples are investigated to illustrate the correctness and efficiency of the proposed method. Damage identification results from measured natural frequencies and the dynamic responses from different excitation forces are compared. The effects of measurement noise on the identification results are investigated. Studies in this paper indicate that the proposed method is efficient and robust for identifying damages in the axially functionally graded beams. Good identified results can be obtained from the short time histories of a few number of measurement points and the first several natural frequencies.
Keywords:Solid mechanics; Damage identification; Axially functionally graded beam; Hybrid sensitivity matrix; Model updating
 
 
 

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