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Smart Structures and Systems Volume 38, Number 1, July 2026 (Special Issue) pages 29-56 DOI: https://doi.org/10.12989/sss.2026.38.1.029 |
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EMD-wavelet joint denoising combined with curvature mode difference for bridge damage identification in complex noise environments |
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Yue Cao, Xiancheng Liu, Bingqian Li, Xiaowei Zhang, Zhanfei Wang, Longsheng Bao
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| Abstract | ||
| To address the problems of low accuracy and weak anti-interference ability in bridge damage identification under complex noise environments, a damage identification method combining EMD-wavelet threshold joint denoising with curvature mode difference is proposed. First, empirical mode decomposition (EMD) adaptively separates high-frequency noise components from useful features in vibration signals. Subsequently, precise denoising is achieved using 3-level decomposition with db4 wavelet basis function and soft threshold processing. Finally, damage localization and quantitative prediction of damage severity are accomplished based on the curvature mode difference index. A finite element model is established using a 147 m-span variable-section continuous beam bridge as the prototype, and four types of working conditions involving single/double damage locations and different damage severities are designed. Numerical simulation verification is conducted under 5% intensity Gaussian white noise interference, with comparisons made against the single wavelet threshold denoising method and the EMD-SVD (Singular Value Decomposition) joint method. Results show that the single wavelet threshold method fails in multiple working conditions under noisy environments, while the proposed joint method effectively suppresses noise interference. It achieves 100% damage localization accuracy and controls the damage severity identification error within 3%~8.7%, improving accuracy by over 50% compared to the single wavelet threshold method. Additionally, it exhibits good adaptability to the complex structure of variable-section continuous beam bridges without requiring complex parameter adjustments, providing an efficient and reliable technical solution for bridge structural health monitoring. | ||
| Key Words | ||
| anti-noise performance; bridge damage identification; curvature mode difference; empirical mode decomposition; wavelet threshold denoising | ||
| Address | ||
| (1) Yue Cao, Xiaowei Zhang, Zhanfei Wang, Longsheng Bao: School of Transportation and Geomatics Engineering, Shenyang Jianzhu University, Shenyang, 110168, China; (2) Xiancheng Liu: College of Mechanical and Electrical Engineering, Hohai University, Changzhou, 213200, China; (3) Bingqian Li: Liaoning Province Expressway Operation and Management Co., Ltd., Shenyang, 110055, China. | ||