基于WOA-VMD的桥梁监测挠度数据处理方法

Data Processing Method of Bridge Deflection Monitoring Based on WOA-VMD

为实现桥梁监测系统中挠度数据的温度效应分离,提出一种自适应数据特征的分解算法。该方法综合应用了鲸鱼优化算法(Whale Optimization Algorithm,WOA)和变分模态分解(Variational Mode Decomposition,VMD),简称WOA-VMD。通过仿真算例得出WOA-VMD提取的日、年温差效应拟合精度与改进的经验模态分解(Empirical Mode Decomposition,EMD)相比,分别提高了56.35%和24.48%。采用WOA-VMD处理洛溪大桥扩建斜拉桥的主梁跨中挠度监测数据,结果表明左右两幅桥的日温差效应与结构温度相关性比改进的EMD高7.37%和5.72%,验证了建议方法的有效性和可靠性。

In order to realize temperature effect separation of deflection data in bridge monitoring system,an adaptive data feature decomposition algorithm is proposed.In this method,Whale Optimization Algorithm and Variational Mode Decomposition are integrated.The simulation results show that the fitting accuracy of the daily and annual temperature difference effect extracted by WOA-VMD is 56.35%and 24.48%higher than that of the improved Empirical Mode Decomposition.The WOA-VMD was used to process the monitoring data of mid-span deflection of the main beam of the extended cable-stayed bridge of Luoxi Bridge.The results show that the correlation between the daily temperature difference effect and structure temperature of the left and right Bridges is 7.37%and 5.72%higher than that of the improved EMD,which verifies the effectiveness and reliability of the proposed method.