Xiong Chunbao, Shang Zhi, Wang Meng, Lian Sida
School of Civil Engineering, Tianjin University, Tianjin 300350, China.
School of Civil Engineering, Hebei University of Engineering, Handan 056009, China.
Sensors (Basel). 2025 Jun 13;25(12):3723. doi: 10.3390/s25123723.
This study focused on the monitoring of a bridge using the global navigation satellite system real-time kinematic (GNSS-RTK) sensor. An improved hybrid denoising method was developed to enhance the GNSS-RTK's accuracy. The improved hybrid denoising method consists of the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), the detrended fluctuation analysis (DFA), and an improved wavelet threshold denoising method. The stability experiment demonstrated the superiority of the improved wavelet threshold denoising method in reducing the noise of the GNSS-RTK. A noisy simulation signal was created to assess the performance of the proposed method. Compared to the ICEEMDAN method and the CEEMDAN-WT method, the proposed method achieves lower RMSE and higher SNR. The signal obtained by the proposed method is similar to the original signal. Then, GNSS-RTK was used to monitor a bridge in maintenance and rehabilitation construction. The bridge monitoring experiment lasted for four hours. (Considering the space limitation of the article, only representative 600 s data is displayed in the paper.) The bridge is located in Tianjin, China. The original displacement ranges are -14.919.3 in the north-south direction; -26.924.7 in the east-west direction; and -46.752.3 in the vertical direction. The displacement ranges processed by the proposed method are -12.317.2 in the north-south direction; -24.624.1 in the east-west direction; and -46.751.1 in the vertical direction. The proposed method processed fewer displacements than the initial monitoring displacements. It indicates the proposed method reduces noise significantly when monitoring the bridge based on the GNSS-RTK sensor. The average sixth-order frequency from PSD is 1.0043 Hz. The difference between the PSD and FEA is only 0.99%. The sixth-order frequency from the PSD is similar to that from the FEA. The lower modes' natural frequencies from the PSD are smaller than those from the FEA. It illustrates the fact that, during the repair process, the missing load-bearing rods made the bridge less stiff and strong. The smaller natural frequencies of the bridge, the complex construction environment, the diversity of workers' operations, and some unforeseen circumstances occurring in the construction all bring risks to the safety of the bridge. We should pay more attention to the dynamic monitoring of the bridge during construction in order to understand the structural status in time to prevent accidents.
本研究聚焦于利用全球导航卫星系统实时动态(GNSS-RTK)传感器对一座桥梁进行监测。开发了一种改进的混合去噪方法以提高GNSS-RTK的精度。改进的混合去噪方法由改进的自适应噪声完备总体经验模态分解(ICEEMDAN)、去趋势波动分析(DFA)以及一种改进的小波阈值去噪方法组成。稳定性实验证明了改进的小波阈值去噪方法在降低GNSS-RTK噪声方面的优越性。创建了一个含噪模拟信号来评估所提方法的性能。与ICEEMDAN方法和CEEMDAN-WT方法相比,所提方法实现了更低的均方根误差(RMSE)和更高的信噪比(SNR)。所提方法获得的信号与原始信号相似。然后,利用GNSS-RTK对一座处于维护和修复施工中的桥梁进行监测。桥梁监测实验持续了4小时。(考虑到文章篇幅限制,论文中仅展示了具有代表性的600秒数据。)该桥梁位于中国天津。原始位移范围在南北方向为-14.919.3;东西方向为-26.924.7;垂直方向为-46.752.3。所提方法处理后的位移范围在南北方向为-12.317.2;东西方向为-24.624.1;垂直方向为-46.751.1。所提方法处理的位移比初始监测位移更少。这表明所提方法在基于GNSS-RTK传感器监测桥梁时能显著降低噪声。功率谱密度(PSD)得出的平均六阶频率为1.0043赫兹。PSD与有限元分析(FEA)之间的差异仅为0.99%。PSD得出的六阶频率与FEA得出的值相似。PSD得出的较低阶模态固有频率比FEA得出的更小。这说明了在修复过程中,缺失的承重杆使桥梁的刚度和强度降低。桥梁固有频率越小、施工环境复杂、工人操作多样以及施工中出现的一些不可预见情况都给桥梁安全带来风险。我们在施工期间应更加关注桥梁的动态监测,以便及时了解结构状态,预防事故发生。