详细信息
A sparse optimization model of l2-l1-2 for estimating guided waves dispersion curves ( SCI-EXPANDED收录 EI收录)
文献类型:期刊文献
英文题名:A sparse optimization model of l2-l1-2 for estimating guided waves dispersion curves
作者:Liu, Zenghua[1];Wang, Meiling[1];Zhu, Yanping[1];Liu, Xiaoyu[2];Hu, Yue[3,4];He, Cunfu[1]
机构:[1]Beijing Univ Technol, Sch Informat Sci & Technol, Beijing 100124, Peoples R China;[2]Beijing Univ Technol, Coll Mech & Energy Engn, Beijing 100124, Peoples R China;[3]East China Univ Sci & Technol, Sch Mech & Power Engn, Shanghai 200237, Peoples R China;[4]East China Univ Sci & Technol, Shanghai Key Lab Intelligent Sensing & Detect Tech, Shanghai 200237, Peoples R China
年份:2025
卷号:253
外文期刊名:MEASUREMENT
收录:;EI(收录号:20251418189018);WOS:【SCI-EXPANDED(收录号:WOS:001463229800001)】;
基金:This work was supported by the National Natural Science Foundation of China (No. 12172015, 11772014 and 52405574) .
语种:英文
外文关键词:Guided waves; Dispersion curves estimation; Sparse reconstruction
摘要:Accurate dispersion information from ultrasonic guided waves is crucial for detecting and localizing damage, especially in complex structures or materials with unknown properties. This study proposes a sparse optimization model, denoted as l(2)-l(1-2), to estimate the dispersion curves using minimal experimental measurement points. The l(2)-l(1-2) model is formulated based on the propagation characteristics of guided waves, and the l(1-2) norm is combined as a regularization term within a least square framework to enhance the sparsity of the solution. The parameters of the l(2)-l(1-2) model are optimized using the correlation coefficient and the relative l(2) norm error as quantitative metrics to improve the quality of the reconstruction. Furthermore, an improved Canny edge detection method is proposed to accurately extract dispersion curves from the reconstruction results. The accuracy and efficiency of the proposed method are verified through experiments on complex U-shaped boom structures and Carbon fiber reinforced polymers, demonstrating the robustness of the method in practical applications.
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