Structural Crack Identification Based on the Variational Mode Decomposition

YANG Qihang; LI Lin’an; LI Liqing; MI Shaoxuan

doi:10.21656/1000-0887.420338

Volume 43 Issue 12

Dec. 2022

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Article Navigation > Applied Mathematics and Mechanics > 2022 > 43(12): 1324-1335

YANG Qihang, LI Lin’an, LI Liqing, MI Shaoxuan. Structural Crack Identification Based on the Variational Mode Decomposition[J]. Applied Mathematics and Mechanics, 2022, 43(12): 1324-1335. doi: 10.21656/1000-0887.420338

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Structural Crack Identification Based on the Variational Mode Decomposition

doi: 10.21656/1000-0887.420338

School of Mechanical Engineering, Tianjin University, Tianjin 300354, P.R.China

Received Date: 2021-11-08
Accepted Date: 2022-04-01
Rev Recd Date: 2022-03-04

Available Online: 2022-11-15

Publish Date: 2022-12-01

Abstract

Abstract

In order to enrich the bridge damage detection method and further improve the accuracy of bridge damage identification, a detection method for simply supported beams with cracks under dynamic loads was proposed not based on the complete finite element model. Under the premise of not blocking traffic, the method only needs to analyze and deal with the acceleration responses of the simply supported beam span, which reduces the mounting, dismounting and maintenance of sensors in practical engineering. At the same time, based on the model, an analytical formula of the acceleration at the midspan of the simply supported cracked beam was derived. Based on the theoretical derivation, the instantaneous energy and the mean energy difference were constructed through the variational mode decomposition and the Hilbert transform, and these 2 crack identification indexes were used to effectively identify small cracks with a crack depth ratio of only 5%. Then the influences of different wheel loads, environmental noises and damage degrees on detection results were studied. The results show that: ① the instantaneous frequency has a better recognition effect for crack positions; ② the mean energy difference is sensitive to crack depth ratio δ and the wheel load magnitude; ③ this method has strong noise robustness.
- variational mode decomposition,
- acceleration response,
- damage identification,
- wheel load

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References(19)

References

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