Structural Damage Identification Based on the Wavelet Transform and Improved Particle Swarm Optimization Algorithm
A method based on the wavelet transform and improved particle swarm optimization (WIPSO) algorithm is proposed to identify the microdamage of structures. First, the singularity of wavelet coefficients is used to identify the structural damage location, and then, the improved particle swarm optimizat...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2020/8869810 |
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| _version_ | 1850173339891400704 |
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| author | Jia Guo Deqing Guan Jianwei Zhao |
| author_facet | Jia Guo Deqing Guan Jianwei Zhao |
| author_sort | Jia Guo |
| collection | DOAJ |
| description | A method based on the wavelet transform and improved particle swarm optimization (WIPSO) algorithm is proposed to identify the microdamage of structures. First, the singularity of wavelet coefficients is used to identify the structural damage location, and then, the improved particle swarm optimization (IPSO) algorithm is used to calculate the optimal solution of the objective function of the structural damage location to determine the structural damage severity. To study the performance of WIPSO, the structural microdamage severity is set within 10%, and a numerical simulation and experimental structure under different damage scenarios are considered. In addition, the ability of wavelet coefficients to identify the location of the structural damage under different noise levels is studied. To evaluate the performance of IPSO, the standard particle swarm optimization algorithm with an inertia weight factor of 0.8 (0.8PSO), the genetic algorithm (GA), and the bat algorithm (BA) are also considered. The results show that WIPSO can effectively and accurately identify the structural damage location and severity. Wavelet transform is very robust to the structural damage location. Compared with the standard 0.8PSO and other mainstream algorithms, IPSO has good convergence and performs more stable and more accurate in the identification of structural damage severity. |
| format | Article |
| id | doaj-art-d36a04addb744e32ba2b88b65e7a7f9c |
| institution | OA Journals |
| issn | 1687-8086 1687-8094 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-d36a04addb744e32ba2b88b65e7a7f9c2025-08-20T02:19:51ZengWileyAdvances in Civil Engineering1687-80861687-80942020-01-01202010.1155/2020/88698108869810Structural Damage Identification Based on the Wavelet Transform and Improved Particle Swarm Optimization AlgorithmJia Guo0Deqing Guan1Jianwei Zhao2Department of Civil Engineering, Changsha University of Science & Technology, Changsha, Hunan, ChinaDepartment of Civil Engineering, Changsha University of Science & Technology, Changsha, Hunan, ChinaDepartment of Civil Engineering, Changsha University of Science & Technology, Changsha, Hunan, ChinaA method based on the wavelet transform and improved particle swarm optimization (WIPSO) algorithm is proposed to identify the microdamage of structures. First, the singularity of wavelet coefficients is used to identify the structural damage location, and then, the improved particle swarm optimization (IPSO) algorithm is used to calculate the optimal solution of the objective function of the structural damage location to determine the structural damage severity. To study the performance of WIPSO, the structural microdamage severity is set within 10%, and a numerical simulation and experimental structure under different damage scenarios are considered. In addition, the ability of wavelet coefficients to identify the location of the structural damage under different noise levels is studied. To evaluate the performance of IPSO, the standard particle swarm optimization algorithm with an inertia weight factor of 0.8 (0.8PSO), the genetic algorithm (GA), and the bat algorithm (BA) are also considered. The results show that WIPSO can effectively and accurately identify the structural damage location and severity. Wavelet transform is very robust to the structural damage location. Compared with the standard 0.8PSO and other mainstream algorithms, IPSO has good convergence and performs more stable and more accurate in the identification of structural damage severity.http://dx.doi.org/10.1155/2020/8869810 |
| spellingShingle | Jia Guo Deqing Guan Jianwei Zhao Structural Damage Identification Based on the Wavelet Transform and Improved Particle Swarm Optimization Algorithm Advances in Civil Engineering |
| title | Structural Damage Identification Based on the Wavelet Transform and Improved Particle Swarm Optimization Algorithm |
| title_full | Structural Damage Identification Based on the Wavelet Transform and Improved Particle Swarm Optimization Algorithm |
| title_fullStr | Structural Damage Identification Based on the Wavelet Transform and Improved Particle Swarm Optimization Algorithm |
| title_full_unstemmed | Structural Damage Identification Based on the Wavelet Transform and Improved Particle Swarm Optimization Algorithm |
| title_short | Structural Damage Identification Based on the Wavelet Transform and Improved Particle Swarm Optimization Algorithm |
| title_sort | structural damage identification based on the wavelet transform and improved particle swarm optimization algorithm |
| url | http://dx.doi.org/10.1155/2020/8869810 |
| work_keys_str_mv | AT jiaguo structuraldamageidentificationbasedonthewavelettransformandimprovedparticleswarmoptimizationalgorithm AT deqingguan structuraldamageidentificationbasedonthewavelettransformandimprovedparticleswarmoptimizationalgorithm AT jianweizhao structuraldamageidentificationbasedonthewavelettransformandimprovedparticleswarmoptimizationalgorithm |