An Enhancing Particle Swarm Optimization Algorithm (EHVPSO) for damage identification in 3D transmission tower

被引:48
作者
Hoang-Le Minh [1 ,2 ]
Khatir, Samir [1 ]
Wahab, Magd Abdel [3 ,4 ]
Thanh Cuong-Le [2 ]
机构
[1] Univ Ghent, Fac Engn & Architecture, Dept Elect Energy Met Mech Construct & Syst, B-9000 Ghent, Belgium
[2] Ho Chi Minh City Open Univ, Fac Civil Engn, Ho Chi Minh City, Vietnam
[3] Ho Chi Minh City Univ Technol HUTECH, CIRTech Inst, Ho Chi Minh City, Vietnam
[4] Univ Ghent, Fac Engn & Architecture, Soete Lab, Technol Pk Zwijnaarde 903, B-9052 Zwijnaarde, Belgium
关键词
SAP2000 (OAPI); Model updating; damage detection; Particle swarm optimization; Structural health monitoring; structural; STRUCTURAL DAMAGE; FLEXIBILITY; VIBRATION; MODEL; FREQUENCY;
D O I
10.1016/j.engstruct.2021.112412
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
In this paper, a novel enhanced Particle Swarm Optimization (PSO) algorithm is introduced for solving damage identification problems. For the first time, the algorithm is applied to a complex structure, namely an Electric Power Transmission with 44.05 m height. The process of structural damage assessment is implemented using SAP2000 commercial software combined with MATLAB. Using the Open Application Programming Interface (OAPI) source code, which is available in SAP2000, a strong MATLAB environment program has been developed in this research. This program can allow the user to adjust the initial model's parameters in SAP2000 to create a continuous two ways data exchange between SAP2000 and MATLAB. Thus, the process of detecting the location and level of damage in the structure is performed by applying a new version of PSO, namely Enhancing Particle Swarm Optimization Algorithm (EHVPSO), using stochastic parameters. The key factor in the EHVPSO algorithm is to introduce two novel equations. The first equation is used to control the convergence rate in each movement of particle ith, and the second equation is used to control the balance between local optimal value and global optimal value. The results demonstrate that the proposed algorithm can detect damage with very high accuracy and reliability.
引用
收藏
页数:23
相关论文
共 58 条
[1]   Damage localization in irregular shape structures using intelligent FE model updating approach with a new hybrid objective function and social swarm algorithm [J].
Alkayem, Nizar Faisal ;
Cao, Maosen ;
Ragulskis, Minvydas .
APPLIED SOFT COMPUTING, 2019, 83
[2]   Damage Diagnosis in 3D Structures Using a Novel Hybrid Multiobjective Optimization and FE Model Updating Framework [J].
Alkayem, Nizar Faisal ;
Cao, Maosen ;
Ragulskis, Minvydas .
COMPLEXITY, 2018,
[3]   Damage identification in three-dimensional structures using single-objective evolutionary algorithms and finite element model updating: evaluation and comparison [J].
Alkayem, Nizar Faisal ;
Cao, Maosen .
ENGINEERING OPTIMIZATION, 2018, 50 (10) :1695-1714
[4]   Structural damage detection using finite element model updating with evolutionary algorithms: a survey [J].
Alkayem, Nizar Faisal ;
Cao, Maosen ;
Zhang, Yufeng ;
Bayat, Mahmoud ;
Su, Zhongqing .
NEURAL COMPUTING & APPLICATIONS, 2018, 30 (02) :389-411
[5]   Automated parameter tuning in one-class support vector machine: an application for damage detection [J].
Anaissi A. ;
Khoa N.L.D. ;
Wang Y. .
International Journal of Data Science and Analytics, 2018, 6 (04) :311-325
[6]  
Barman S.K., 2020, Recent Advances in Theoretical, Applied, Computational and Experimental Mechanics, P277, DOI DOI 10.1007/978-981-15-1189-9_23
[7]   Defining a standard for particle swarm optimization [J].
Bratton, Daniel ;
Kennedy, James .
2007 IEEE SWARM INTELLIGENCE SYMPOSIUM, 2007, :120-+
[8]  
Chen C-H, 2011, ADV ARTIF INTELL, V2011
[9]   A hybrid ant lion optimizer with improved Nelder-Mead algorithm for structural damage detection by improving weighted trace lasso regularization [J].
Chen, Chengbin ;
Yu, Ling .
ADVANCES IN STRUCTURAL ENGINEERING, 2020, 23 (03) :468-484
[10]   Analysis of particle interaction in particle swarm optimization [J].
Chen, Ying-ping ;
Jiang, Pei .
THEORETICAL COMPUTER SCIENCE, 2010, 411 (21) :2101-2115