Improved Continuous Wavelet Transform for Modal Parameter Identification of Long-Span Bridges
Accurate and timely identification of modal parameters of long-span bridges is important for bridge health monitoring and wind tunnel tests. Wavelet analysis is one of the most advantageous methods for identification because of its good localization characteristics in both time and frequency domain....
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2020/4360184 |
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| _version_ | 1832553669424316416 |
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| author | Mingjin Zhang Xu Huang Yongle Li Hao Sun Jingyu Zhang Bin Huang |
| author_facet | Mingjin Zhang Xu Huang Yongle Li Hao Sun Jingyu Zhang Bin Huang |
| author_sort | Mingjin Zhang |
| collection | DOAJ |
| description | Accurate and timely identification of modal parameters of long-span bridges is important for bridge health monitoring and wind tunnel tests. Wavelet analysis is one of the most advantageous methods for identification because of its good localization characteristics in both time and frequency domain. In recent years, the wavelet method has been applied more frequently in parameter identification of linear and nonlinear systems. In this article, based on wavelet ridges and wavelet skeleton, the improved modal parameter identification method was studied. To find the appropriate time-frequency resolution, an optimal wavelet basis design principle based on minimum Shannon entropy was proposed. Aiming at endpoint effect in wavelet transform, a prediction continuation method based on support vector machine (SVM) was proposed, which can effectively suppress the endpoint effect of the extended samples. In view of the fact that the ridges of metric matrices obtained by the traditional crazy climber algorithm cannot fully reflect the distribution of ridges of modulus value matrices of wavelet coefficients, an improved high-precision crazy climber algorithm was put forward to accurately identify the position of the ridge of wavelet coefficients. Finally, taking a long-span cable-stayed bridge and a long-span suspension bridge as the engineering background, improved continuous wavelet transform (CWT) was applied to modal parameter identification of bridge under ambient excitation. The modal parameters such as modal frequency, damping ratio, and mode shape were obtained. Compared with the calculation value of the numerical simulation of long-span cable-stayed bridge and wind tunnel test of long-span suspension bridge, the reliability of CWT for modal parameter identification of long-span bridges under ambient excitation was verified. |
| format | Article |
| id | doaj-art-2942a5110fc14788af4d5ee641666f6d |
| 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-2942a5110fc14788af4d5ee641666f6d2025-02-03T05:53:25ZengWileyShock and Vibration1070-96221875-92032020-01-01202010.1155/2020/43601844360184Improved Continuous Wavelet Transform for Modal Parameter Identification of Long-Span BridgesMingjin Zhang0Xu Huang1Yongle Li2Hao Sun3Jingyu Zhang4Bin Huang5Department of Bridge Engineering, Southwest Jiaotong University, Chengdu 610031, Sichuan, ChinaDepartment of Bridge Engineering, Southwest Jiaotong University, Chengdu 610031, Sichuan, ChinaDepartment of Bridge Engineering, Southwest Jiaotong University, Chengdu 610031, Sichuan, ChinaCCCC Second Highway Consultants Co., Ltd., Wuhan 430056, Hubei, ChinaDepartment of Bridge Engineering, Southwest Jiaotong University, Chengdu 610031, Sichuan, ChinaSichuan Yakang Expressway Co., Ltd., 610041 Chengdu, Sichuan, ChinaAccurate and timely identification of modal parameters of long-span bridges is important for bridge health monitoring and wind tunnel tests. Wavelet analysis is one of the most advantageous methods for identification because of its good localization characteristics in both time and frequency domain. In recent years, the wavelet method has been applied more frequently in parameter identification of linear and nonlinear systems. In this article, based on wavelet ridges and wavelet skeleton, the improved modal parameter identification method was studied. To find the appropriate time-frequency resolution, an optimal wavelet basis design principle based on minimum Shannon entropy was proposed. Aiming at endpoint effect in wavelet transform, a prediction continuation method based on support vector machine (SVM) was proposed, which can effectively suppress the endpoint effect of the extended samples. In view of the fact that the ridges of metric matrices obtained by the traditional crazy climber algorithm cannot fully reflect the distribution of ridges of modulus value matrices of wavelet coefficients, an improved high-precision crazy climber algorithm was put forward to accurately identify the position of the ridge of wavelet coefficients. Finally, taking a long-span cable-stayed bridge and a long-span suspension bridge as the engineering background, improved continuous wavelet transform (CWT) was applied to modal parameter identification of bridge under ambient excitation. The modal parameters such as modal frequency, damping ratio, and mode shape were obtained. Compared with the calculation value of the numerical simulation of long-span cable-stayed bridge and wind tunnel test of long-span suspension bridge, the reliability of CWT for modal parameter identification of long-span bridges under ambient excitation was verified.http://dx.doi.org/10.1155/2020/4360184 |
| spellingShingle | Mingjin Zhang Xu Huang Yongle Li Hao Sun Jingyu Zhang Bin Huang Improved Continuous Wavelet Transform for Modal Parameter Identification of Long-Span Bridges Shock and Vibration |
| title | Improved Continuous Wavelet Transform for Modal Parameter Identification of Long-Span Bridges |
| title_full | Improved Continuous Wavelet Transform for Modal Parameter Identification of Long-Span Bridges |
| title_fullStr | Improved Continuous Wavelet Transform for Modal Parameter Identification of Long-Span Bridges |
| title_full_unstemmed | Improved Continuous Wavelet Transform for Modal Parameter Identification of Long-Span Bridges |
| title_short | Improved Continuous Wavelet Transform for Modal Parameter Identification of Long-Span Bridges |
| title_sort | improved continuous wavelet transform for modal parameter identification of long span bridges |
| url | http://dx.doi.org/10.1155/2020/4360184 |
| work_keys_str_mv | AT mingjinzhang improvedcontinuouswavelettransformformodalparameteridentificationoflongspanbridges AT xuhuang improvedcontinuouswavelettransformformodalparameteridentificationoflongspanbridges AT yongleli improvedcontinuouswavelettransformformodalparameteridentificationoflongspanbridges AT haosun improvedcontinuouswavelettransformformodalparameteridentificationoflongspanbridges AT jingyuzhang improvedcontinuouswavelettransformformodalparameteridentificationoflongspanbridges AT binhuang improvedcontinuouswavelettransformformodalparameteridentificationoflongspanbridges |