Online Intelligent Identification of Modal Parameters for Large Cable-Stayed Bridges
Realizing online intelligent identification of bridge modal parameters requires not only the adaptive decomposition of structural response signals but also the enforcement of the automatic identification of modal parameters. Therefore, in this paper, the signal decomposition algorithm-ensemble empir...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
|
| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2020/2040216 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841524465557569536 |
|---|---|
| author | Peng Wen Inamullah Khan He Jie Chen Qiaofeng Yang Shiyu |
| author_facet | Peng Wen Inamullah Khan He Jie Chen Qiaofeng Yang Shiyu |
| author_sort | Peng Wen |
| collection | DOAJ |
| description | Realizing online intelligent identification of bridge modal parameters requires not only the adaptive decomposition of structural response signals but also the enforcement of the automatic identification of modal parameters. Therefore, in this paper, the signal decomposition algorithm-ensemble empirical mode decomposition algorithm (EEMD) is improved to fulfill the above task. First, the adaptive matching algorithm is introduced to deal with the endpoint effect; second, the method of classification is used to avoid modal aliasing. Finally, an index for filtering the effective intrinsic mode function (IMF) components is constructed to realize automatic screening and signal reconstruction of the effective IMF components. At the same time, the first derivative of the singular entropy increment is used to automatically determine the order of the system, and then the spectral clustering algorithm is combined with the stochastic subspace algorithm to ultimately reach the goal of automatic identification of modal parameters. |
| format | Article |
| id | doaj-art-760df605d18e4fc1aa8dfa69d3379e63 |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-760df605d18e4fc1aa8dfa69d3379e632025-02-03T05:53:09ZengWileyShock and Vibration1070-96221875-92032020-01-01202010.1155/2020/20402162040216Online Intelligent Identification of Modal Parameters for Large Cable-Stayed BridgesPeng Wen0Inamullah Khan1He Jie2Chen Qiaofeng3Yang Shiyu4Bridge Engineering Department, Southwest Jiaotong University, Chengdu, ChinaSchool of Civil Engineering, National University of Sciences and Technology, Islamabad, PakistanBridge Engineering Department, Southwest Jiaotong University, Chengdu, ChinaBridge Engineering Department, Southwest Jiaotong University, Chengdu, ChinaBridge Engineering Department, Southwest Jiaotong University, Chengdu, ChinaRealizing online intelligent identification of bridge modal parameters requires not only the adaptive decomposition of structural response signals but also the enforcement of the automatic identification of modal parameters. Therefore, in this paper, the signal decomposition algorithm-ensemble empirical mode decomposition algorithm (EEMD) is improved to fulfill the above task. First, the adaptive matching algorithm is introduced to deal with the endpoint effect; second, the method of classification is used to avoid modal aliasing. Finally, an index for filtering the effective intrinsic mode function (IMF) components is constructed to realize automatic screening and signal reconstruction of the effective IMF components. At the same time, the first derivative of the singular entropy increment is used to automatically determine the order of the system, and then the spectral clustering algorithm is combined with the stochastic subspace algorithm to ultimately reach the goal of automatic identification of modal parameters.http://dx.doi.org/10.1155/2020/2040216 |
| spellingShingle | Peng Wen Inamullah Khan He Jie Chen Qiaofeng Yang Shiyu Online Intelligent Identification of Modal Parameters for Large Cable-Stayed Bridges Shock and Vibration |
| title | Online Intelligent Identification of Modal Parameters for Large Cable-Stayed Bridges |
| title_full | Online Intelligent Identification of Modal Parameters for Large Cable-Stayed Bridges |
| title_fullStr | Online Intelligent Identification of Modal Parameters for Large Cable-Stayed Bridges |
| title_full_unstemmed | Online Intelligent Identification of Modal Parameters for Large Cable-Stayed Bridges |
| title_short | Online Intelligent Identification of Modal Parameters for Large Cable-Stayed Bridges |
| title_sort | online intelligent identification of modal parameters for large cable stayed bridges |
| url | http://dx.doi.org/10.1155/2020/2040216 |
| work_keys_str_mv | AT pengwen onlineintelligentidentificationofmodalparametersforlargecablestayedbridges AT inamullahkhan onlineintelligentidentificationofmodalparametersforlargecablestayedbridges AT hejie onlineintelligentidentificationofmodalparametersforlargecablestayedbridges AT chenqiaofeng onlineintelligentidentificationofmodalparametersforlargecablestayedbridges AT yangshiyu onlineintelligentidentificationofmodalparametersforlargecablestayedbridges |