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Influence line identification method of beam bridge based on empirical mode decomposition and Tikhonov regularization
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Yu ZHOU1, 2, 3, Wenqi SHANG1, 2, Deyi WU1, 2, Shengkui DI3, Xu ZHENG4
Journal of Vibration Engineering | 2025, 38(1) : 144 - 153
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Journal of Vibration Engineering | 2025, 38(1): 144-153
Influence line identification method of beam bridge based on empirical mode decomposition and Tikhonov regularization
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Yu ZHOU1, 2, 3, Wenqi SHANG1, 2, Deyi WU1, 2, Shengkui DI3, Xu ZHENG4
Affiliations
  • 1.School of Civil Engineering, Anhui Jianzhu University, Hefei 230601, China
  • 2.National-local Joint Engineering Laboratory of Building Health Monitoring and Disaster Prevention Technology, Anhui Jianzhu University, Hefei 230601, China
  • 3.College of Civil Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
  • 4.School of Civil Engineering, Dalian University of Technology, Dalian 116023, China
Published: 2025-01-10 doi: 10.16385/j.cnki.issn.1004-4523.2025.01.016
Outline
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The deflection influence line and strain influence line can integrally reflect the flexural stiffness of beam bridge section. In the process of obtaining the measured time-history response of beam bridge, the response of beam bridge involves the influence line information and structural dynamic components, and is interfered by the multi-axis effect of loading vehicle under vehicle moving load. In order to identify the influence line of beam bridge structure accurately, the empirical mode decomposition was proposed to eliminate the dynamic component in the measured data of beam bridge, and the quasi-static response data of beam bridge containing the multi-axis effect was obtained. Combined with the sampling frequency and vehicle wheelbase, a mathematical model was established to identify the influence line, and the multi-axis effect of vehicle was converted into unit concentrated load. Tikhonov regularization method was used to accurately solve the stable solution of the influence line of the beam bridge. Through the establishment of numerical simulation models of 1/2 two-axle vehicle-crossing simply-supported beam bridge and three-span continuous beam bridge with variable section, the deflection and strain time-history responses of simply-supported beam bridge and three-span continuous beam bridge at different vehicle speeds were extracted, and the feasibility and effectiveness of identifying influence lines of beam bridge based on empirical mode decomposition and Tikhonov regularization method were verified. The deflection influence lines and strain influence lines of the structural examples of the beam bridge were identified accurately, and the identification effect of the influence lines was evaluated quantitatively by establishing error index. The research also found that the identification effect of the influence lines of the beam bridge decreased with the increase of the loading vehicle speed.

bridge engineering  /  moving load  /  influence line identification  /  empirical mode decomposition  /  Tikhonov regularization
Yu ZHOU, Wenqi SHANG, Deyi WU, Shengkui DI, Xu ZHENG. Influence line identification method of beam bridge based on empirical mode decomposition and Tikhonov regularization[J]. Journal of Vibration Engineering, 2025 , 38 (1) : 144 -153 . DOI: 10.16385/j.cnki.issn.1004-4523.2025.01.016
Year 2025 volume 38 Issue 1
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Article Info
doi: 10.16385/j.cnki.issn.1004-4523.2025.01.016
  • Receive Date:2023-01-06
  • Online Date:2026-02-11
  • Published:2025-01-10
Article Data
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History
  • Received:2023-01-06
  • Revised:2023-03-15
Funding
Affiliations
    1.School of Civil Engineering, Anhui Jianzhu University, Hefei 230601, China
    2.National-local Joint Engineering Laboratory of Building Health Monitoring and Disaster Prevention Technology, Anhui Jianzhu University, Hefei 230601, China
    3.College of Civil Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
    4.School of Civil Engineering, Dalian University of Technology, Dalian 116023, China
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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