Deng Tongfa, Huang Jinwen, Cao Maosen, Li Dayang, Bayat Mahmoud
Jiangxi Province Key Laboratory of Environmental Geotechnical Engineering and Hazards Control, Jiangxi University of Science and Technology, Ganzhou 341000, China.
School of Civil and Surveying & Mapping Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China.
Sensors (Basel). 2021 Dec 29;22(1):239. doi: 10.3390/s22010239.
Curved beam bridges, whose line type is flexible and beautiful, are an indispensable bridge type in modern traffic engineering. Nevertheless, compared with linear bridges, curved beam bridges have more complex internal forces and deformation due to the curvature; therefore, this type of bridge is more likely to suffer damage in strong earthquakes. The occurrence of damage reduces the safety of bridges, and can even cause casualties and property loss. For this reason, it is of great significance to study the identification of seismic damage in curved beam bridges. However, there is currently little research on curved beam bridges. For this reason, this paper proposes a damage identification method based on wavelet packet norm entropy (WPNE) under seismic excitation. In this method, wavelet packet transform is adopted to highlight the damage singularity information, the Lp norm entropy of wavelet coefficient is taken as a damage characteristic factor, and then the occurrence of damage is characterized by changes in the damage index. To verify the feasibility and effectiveness of this method, a finite element model of Curved Continuous Rigid-Frame Bridges (CCRFB) is established for the purposes of numerical simulation. The results show that the damage index based on WPNE can accurately identify the damage location and characterize the severity of damage; moreover, WPNE is more capable of performing damage location and providing early warning than the method based on wavelet packet energy. In addition, noise resistance analysis shows that WPNE is immune to noise interference to a certain extent. As long as a series of frequency bands with larger correlation coefficients are selected for WPNE calculation, independent noise reduction can be achieved.
曲线梁桥线型优美灵活，是现代交通工程中不可或缺的桥型。然而，与直线桥梁相比，曲线梁桥由于曲率的存在，内力和变形更为复杂；因此，这类桥梁在强震中更容易遭受破坏。破坏的发生降低了桥梁的安全性，甚至可能导致人员伤亡和财产损失。为此，研究曲线梁桥的地震损伤识别具有重要意义。然而，目前针对曲线梁桥的研究较少。因此，本文提出一种基于地震激励下小波包范数熵（WPNE）的损伤识别方法。该方法采用小波包变换突出损伤奇异信息，将小波系数的Lp范数熵作为损伤特征因子，然后通过损伤指标的变化来表征损伤的发生。为验证该方法的可行性和有效性，建立了曲线连续刚构桥（CCRFB）有限元模型进行数值模拟。结果表明，基于WPNE的损伤指标能够准确识别损伤位置并表征损伤程度；此外，与基于小波包能量的方法相比，WPNE在损伤定位和预警方面更具优势。另外，抗噪分析表明，WPNE在一定程度上不受噪声干扰。只要选择一系列相关系数较大的频带进行WPNE计算，就能实现独立降噪。
