基于经验小波变换 (EWT) 及改进随机减量技术, 推导了一种环境风激励下超高结构模态参数的时频分析算法。 该方法首先对实测信号进行 EWT 分解, 获得单模态分量, 然后采用改进随机减量技术得到自由衰减响应, 最后利用希尔伯特变换和线性拟合计算结构的自振频率和阻尼比。 通过五层框架结构的数值算例验证了该方法的有效性, 并利用该方法对台风 "妮妲" 作用下深圳平安金融中心的实测加速度进行时频分析, 获得了深圳平安金融中心的阻尼比及自振频率, 揭示了该超高结构模态参数瞬时变化特征。 所识别的结构各阶频率略高于 0.1Hz、平动阻尼比在 1% 以内的研究成果为超高层建筑健康监测和振动控制提供了依据和资料。

In this paper, a new modal parameter identification approach for super tall buildings is developed based on empirical wavelet transform (EWT) and improved random decrement technique (IRDT). In the proposed method, the measured vibration signal is firstly decomposed by EWT to obtain several single mode signals. Secondly, the free vibration response of each mono component is obtained by using IRDT. Finally, Hilbert transform and curve fitting are used to estimate the natural frequency and damping ratio. A numerical simulation of a five-story frame structure is employed to validate the effectiveness of the proposed method. Furthermore, the measured acceleration responses of Ping’an Financial Center during Typhoon Nida are analyzed based on the newly developed approach. The damping ratios and vibration frequencies of Ping’an Financial Center are identified. Meanwhile, the instantaneous characteristics (including the instantaneous amplitude and the instantaneous frequency) of the building are investigated in details. Each natural frequency of this building is above 0.1Hz, and corresponding damping ratios of the translational mode are all below 1%. The findings of this study are valuable for health monitoring and vibration control of super tall structures.