## 1 Introduction  
In structural dynamic engineering, the development of reasonable finite element (FE) models can guarantee the reliability of structural analysis. Nevertheless, discrepancies always exist between measured and FE model predicted behaviors, which can be due to uncertain structure properties, model simplification, and/or inappropriate boundary conditions [1]. Therefore, to reduce these discrepancies, it is often required to update the FE model based on measured data. The dynamic model-updating method is an important means of building a high-precision FE model, which can reflect the dynamic characteristics of the actual structure. In general, dynamic model-updating methods can be classified into matrix updating methods, also called direct updating methods [2; 3; 4], and parameter updating methods, also called iterative updating methods [5; 6; 7]. In matrix updating methods, the mass, stiffness, and damping matrices of the FE model are directly updated to generate an accurate model by one-step procedure calculation, which is a very efficient approach. However, the matrices of the updated model lose the symmetry and sparsity of the original matrices, leading to lack of physical significance [4]. Thus, in practice, it is difficult to use these methods to provide clear guidance. In contrast, parameter updating methods update the FE model by adjusting physical parameters, such as material parameters, geometric dimensions, and connection and support stiffness characteristics, which are much closer to physically realizable quantities [5]. Compared to matrix updating methods, parameter updating methods are more acceptable and have been widely used in structural engineering. In a sense, the model-updating process using the parameter updating method can be regarded as an optimization problem, since model-updating is essentially an inverse problem, where a deterministic objective function is adopted to obtain the precise FE model by seeking the optimal structural parameters [8]. Consequently, in parameter updating methods, a reasonable optimization model and an efficient optimization algorithm are the key elements to obtaining an accurate FE model. It is common knowledge that the development of an optimization model generally includes three factors: design variables, objective functions, and constraints. In model-updating, the primary issue concerns the construction of the objective function, which aims to quantify the discrepancies between the actual structure and the corresponding FE model. Frequency response functions (FRFs) [9; 10; 11; 12] and modal parameters, such as natural frequencies and mode shapes [13; 14; 15], are commonly used to form the objective function. FRF data of the actual structure obtained from vibration tests can be directly applied in model-updating. The FRF-based model-updating method was first proposed by [PERSON] and [PERSON] [16; 17], who successfully applied it to update undamped structure models. Subsequently, this method was further extended by [PERSON] [9] to update linear damping models. Since then, FRF-based model-updating methods have been further developed. However, measurements of FRF data are susceptible to environmental noise, which may lead to inaccurate model-updating. Moreover, in practice, FRF data are usually inaccessible, since the excitation loads of engineering structures are often difficult to determine. Such deficiencies hinder the wide application of the FRF-based model-updating method in practical engineering. On the other hand, modal parameters can be easily extracted from the response data of a structure using modal-analysis techniques under operational conditions. In addition, compared to the FRF-based model-updating method, the natural frequency- or mode- shape-based model-updating method uses only few residual discrete frequencies of interest, thus avoiding the unnecessary time consumption induced by the use of uninteresting frequency data over the entire frequency domain.