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Author:

Liu, Caiwei (Liu, Caiwei.) | Zhang, Yigang (Zhang, Yigang.) | Wu, Jinzhi (Wu, Jinzhi.)

Indexed by:

EI Scopus PKU CSCD

Abstract:

Although spatial lattice structure is consisted of numerous tubes in general, those tubes always are combined in certain knowable rules. A three-ste Pstrategy for damage location of spatial lattice structure is proposed. To verify the feasibility of the proposed method, a test model of single-layer cylindrical reticulated shell with 157 bolt-ball joints and 414 tubes were assembled. The vibration characteristics of this test model both in good condition and in five damage cases were measured. Different tubes were removed in the five damage cases and a fine finite element model was built. With the first three measured modal data from a part of measuring points on this test model as input parameters of the neural network model, different damage of the model is identified. Based on the measured low-order incomplete modal parameters, this strategy can simplify the neural network and its identification effect is also good, which validates the feasibility of the proposed method.

Keyword:

Cylinders (shapes) Dynamic analysis Shells (structures) Location Multilayer neural networks Joints (structural components) Experiments Neural networks Modal analysis

Author Community:

  • [ 1 ] [Liu, Caiwei]Spatial Structures Research Center, Beijing University of Technology, Beijing 100124, China
  • [ 2 ] [Zhang, Yigang]Spatial Structures Research Center, Beijing University of Technology, Beijing 100124, China
  • [ 3 ] [Zhang, Yigang]Key Laboratory of Urban Security and Disaster Engineering of the Ministry of Education, Beijing University of Technology, Beijing 100124, China
  • [ 4 ] [Wu, Jinzhi]Spatial Structures Research Center, Beijing University of Technology, Beijing 100124, China

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Source :

China Civil Engineering Journal

ISSN: 1000-131X

Year: 2014

Issue: 6

Volume: 47

Page: 9-17

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 4

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