A rapid identification method for underwater explosion damage of a concrete gravity dam

被引：0
|
作者：
Li Q. [1 ,2 ,3 ]
Wang G. [2 ]
Lu W. [2 ]
Niu X. [1 ]
Gu C. [3 ]
机构：
[1] Changjiang Institute of Survey, Planning, Design and Research, Wuhan
[2] State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan
[3] College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing
来源：
Wang, Gaohui | 1600年 / Chinese Vibration Engineering Society卷 / 39期
关键词：
Anti-noise performance; Concrete gravity dam; Damage identification; Particle swarm optimization(PSO); Support vector regression(SVR);
D O I：
10.13465/j.cnki.jvs.2020.24.007
中图分类号：
学科分类号：
摘要：
The rapid identification of damage location and range of dams subjected to underwater explosion is difficult to realize using traditional damage identification technology. Through the secondary development of ABAQUS based on Python, the batch establishment of a finite element model and the batch calculation of modal analysis under different damage conditions were realized. The frequency databases of gravity dams at different damage locations and ranges were constructed. Particle swarm optimization (PSO) was used to find the optimal training parameters of single and multiple damage identification of dams via a support vector regression (SVR) model, and the model was validated by the test samples of the frequency database. Considering the influence of different noise levels on damage identification, the reliability of SVR models for dam damage identification was evaluated. The results show that the PSO-SVR model can accurately identify the damage location and radius of gravity dams, and the model has good anti-noise performance. © 2020, Editorial Office of Journal of Vibration and Shock. All right reserved.
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页码：46 / 53and62
页数：5316
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