环境激励下桥梁结构信号分解与模态参数识别

为实现环境激励下桥梁结构信号分解与模态参数识别的一体化,首先,针对现有集合经验模态分解算法存在的端点效应和有效本征模态函数筛选难的问题,通过引入镜像延拓算法和支持向量回归机算法来抑制端点效应,并根据互相关系数和能量系数建立筛选有效本征模态函数的新指标——有效系数;其次,根据桥梁结构真实模态存在的一般规律提出了用于智能化辨识稳定图中真实模态的算法;最后,通过某大型斜拉桥振动台试验来验证所提算法的可行性。结果表明,所提算法不仅能实现桥梁结构响应信号的自适应分解和重构,还能实现稳定图中真实模态的智能化筛选,即实现桥梁结构模态参数的智能化识别,且识别结果具有可靠性。

Signal Decomposition and Modal Parameter Identification for Bridge Structural Under Environmental Excitation

In order to realize the adaptive decomposition and reconstruction for the response signals of bridge structure as well as the intelligent identification of mode parameters, a novel corresponding improved method is proposed by aiming at the existing ensemble empirical mode decomposition(EEMD)and intrinsic mode function for the problems of existing end effects and selecting effective modal functions difficultly. Based on the introducing the mirror extension method and the support vector regression algorithm, the endpoint effects is suppressed. And at the same time, in accordance with the cross correlation coefficient and energy coefficient, a new index of effective intrinsic mode function is established. Moreover, the algorithm for intelligent identification of the real modal state is proposed based on the general rule of the true mode of bridge structure. The effectiveness of the proposed method is verified by a shaking table test for a model of cable-stayed bridge. The verified results showes that the proposed method can not only realize adaptive decomposition and reconstruction of response signals for bridge structural, but also can implement intelligent screening of the real modal in the stable graph, that is realize the intelligent identification of modal parameter for the bridge structural, and its recognition results are reliable.