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Structural damage detection using imperialist competitive algorithm and damage function

delete2019-04-01
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PRE
AI
M
Mahmoud R. Maheri *
DOI:10.1016/j.asoc.2018.12.032delete
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摘要

摘要

En 中文
In practical damage detection problems, experimental modal data is only available for a limited number of modes and in each mode, only a limited number of nodal points are recorded. In using modal data, the majority of the available damage detection solution techniques either require data for all the modes, or all the nodal data for a number of modes; neither of which may be practically available through experiments. In the present study, damage identification is carried out using only a limited number of nodal data of a limited number of modes. The proposed method uses the imperialist competitive optimization algorithm and damage functions. To decrease the number of design variables, several bilinear damage functions are defined to model the damage distribution. Damage functions with both variable widths and variable weights are proposed for increased accurately. Four different types of objective functions which use modal responses of damaged structure are investigated with the aim of finding the most suitable function. The efficiency of the proposed method is investigated using three benchmark numerical examples using both clean and noisy modal data. It is shown that by only using a limited number of modal data, the proposed method is capable of accurately detecting damage locations and reasonably accurately evaluate their extents. The proposed algorithm is most effective with noisy modal data, compared to other available solutions. (C) 2019 Elsevier B.V. All rights reserved.
Keyword:
Damage detection
Imperialist competitive method
Damage function
Modal data
Noisy response
Finite element method
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期刊

Applied Soft Computing 封面图
Applied Soft Computing
IF:
6.6
论文数:
1.4W
被引数:
4.8W

机构

S
Shiraz University
学者数:
8.1K
论文数: 7.5K
被引数: 7.4K
引用论文

引用论文

Structural damage detection based on posteriori probability support vector machine and Dempster-Shafer evidence theory
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PREAI
errZhou, Qifeng; Zhou, Hao; Zhou, Qingqing; Yang, Fan; Luo, Linkai; Li, Tao
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