The determination of accurate global and local modal information is a crucial step for any structural model updating process. Existing modal extraction methods can extract global modal properties with a good degree of accuracy; however, they have difficulties extracting accurate local information under transient excitations. This work presents a novel method, called Extended Empirical Wavelet Transformation (EEWT), in which local and global modal information can be accurately obtained under transient excitations. A new objective function is proposed in this work, where the local and global information resulting from EEWT can be used inside an optimization problem to solve for structures’ unknown parameters and boundary coefficients using a minimum number of measurement points. The proposed EEWT modal extraction and updating schemes were experimentally implemented in two problems: a simply supported beam and a scaled model of a highway bridge with unknown boundary conditions. The results showed the capability of the proposed approaches to identify structural material properties and boundary coefficients of both systems under transient excitations. Comparison between the proposed method and three other well-known modal extraction methods illustrated the effectiveness of EEWT and showed how the resulting optimum points can be affected by the inaccuracy of the extracted modal information.

确定准确的全局和局部模式信息对于任何结构模型更新过程都是至关重要的一步。现有的模态提取方法可以高度准确地提取全局模态属性。但是，它们在瞬态激励下难以提取准确的局部信息。这项工作提出了一种称为扩展经验小波变换（EEWT）的新方法，其中可以在瞬态激励下准确获得局部和全局模态信息。在这项工作中提出了一个新的目标函数，其中可以将来自EEWT的局部和全局信息用于优化问题中，从而使用最少的测量点来求解结构的未知参数和边界系数。拟议的EEWT模态提取和更新方案在两个问题上通过实验实现：简单支撑梁和边界条件未知的公路桥梁的比例模型。结果表明，所提出的方法能够识别瞬态激励下两个系统的结构材料特性和边界系数。所提出的方法与其他三种众所周知的模态提取方法之间的比较说明了EEWT的有效性，并显示了所提取的模态信息的不准确性如何影响所得的最佳点。结果表明，所提出的方法能够识别瞬态激励下两个系统的结构材料特性和边界系数。所提出的方法与其他三种众所周知的模态提取方法之间的比较说明了EEWT的有效性，并显示了所提取的模态信息的不准确性如何影响所得的最佳点。结果表明，所提出的方法能够识别瞬态激励下两个系统的结构材料特性和边界系数。所提出的方法与其他三种已知的模态提取方法的比较说明了EEWT的有效性，并显示了所提取的模态信息的不准确性如何影响所得的最佳点。