Techno Press
Techno Press

Smart Structures and Systems
  Volume 35, Number 2, February 2025 , pages 101-113
DOI: https://doi.org/10.12989/sss.2025.35.2.101
 


Multimodal data fusion damage identification method in noisy environments: A case study of cable-stayed bridges
Yue Cao, Longsheng Bao, Xiaowei Zhang and Zhanfei Wang

 
Abstract
    To enhance the accuracy of damage detection and prevent misjudgments when applying a single damage index, we have developed a method for multivariate data fusion damage detection based on signal denoising. This approach involves fusing two modal indicators and two vehicle excitation response indicators, ultimately performing a secondary fusion of the combined indicators. A statistical noise reduction method was applied to minimize noise in the fundamental indices. A modebased fusion index was created based on the curvature and displacement modes, whereas a fusion index based on the vehicle excitation response was generated by using the acceleration energy difference and acceleration energy-curvature difference. The Dempster-Shafer evidence theory was utilized for the secondary fusion of multiple fusion indices, leading to higher damage detection accuracy. The effectiveness of the damage identification method was confirmed by the ratio of the sub-peak value to the peak value. Moreover, numerical simulation data from a cable-stayed bridge further validated the damage detection method, showing a significant decrease in the ratio of the sub-peak value to the peak value (i.e., a reduction of 16-99%) after secondary fusion. These results demonstrate the feasibility of this method.
 
Key Words
    cable-stayed bridge; damage detection; Dempster–Shafer evidence theory; noise reduction; statistics
 
Address
(1) Yue Cao:
School of Civil Engineering, Shenyang Jian Zhu University, Shenyang, 110168, China;
(2) Yue Cao, Longsheng Bao, Xiaowei Zhang, Zhanfei Wang:
School of Transportation and Geomatics Engineering, Shenyang Jian Zhu University, Shenyang, 110168, China.
 

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