## 1 Introduction
Nevertheless, model parameter-based model-updating methods are associated with certain problems that need to be tackled. Modal pairs, including natural frequencies and mode shapes, are the most commonly used modal parameters in model-updating, and can be used separately or combined. The natural frequency is the overall attribute of a structure and can be accurately identified from the response data either in the time or frequency domain. In model-updating, the absolute or relative errors of experimental and analytical natural frequencies can be used directly as the objective function [13]. The mode shape is the local attribute of a structure; that is, theoretically, the measurement of complete mode shapes requires countless measuring points, which cannot be realized experimentally. Therefore, an incompleteness problem in measurement data inevitably exists in this type of method since the obtained data contain fewer modes than the order of the identified numerical model [1]. As a result, the modal order measured from the actual structure does not correspond to the numerical model. Furthermore, mode switching mostly occurs in the model-updating process, since not every mode can be changed within the same step [18]. In other words, the sensitivity of each order mode to the structural parameters is different. After multiple iteration steps, the change of adjacent modes could be greater than the difference between them, resulting in mode switching. If mode switching is not taken into consideration, it is likely to disturb the model-updating process and result in an ill-conditioned updated model. In order to overcome the aforementioned problems, it is necessary to assess the consistency between experimental and analytical modes. Compared to the natural frequency of a structure, its mode shape contains rich modal information. Therefore, in the literature, many studies have taken the mode-shape information of the structure as the main criterion to measure the consistency between modes. The modal assurance criterion (MAC) proposed by [PERSON] [19], and its derivatives, can effectively solve this problem, and due to their simple statistical concept, they have become the most widely used quantitative indicators [18]. [PERSON] [13] introduced the coordinate strain modal assurance criterion (COSMAC) to quantify the difference between experimental and numerical strain mode shapes, and combined the error of natural frequencies to construct an objective function for model-updating. In their study, the quantified results of the consistency between experimental and analytical mode shapes were taken as the target for correction, and the optimal value was obtained if and only if the experimental and numerical modes were of the same order mode. [PERSON] [5] proposed an improved MAC to assess the similarity of mode shapes. In the model-updating process, the improved MAC was applied to identify the analytical modes that match the measured modes to ensure the accuracy of the updated numerical model. According to the above studies, the MAC is an effective index to identify or track modes; however, it is not sensitive to mode-shape changes, since all mode-shape differences are considered in the scalar of a single global index [20]. In the sparse mode region in the frequency domain, the similarity of mode shapes in a relatively wide frequency range near each order mode is high. For example, if the frequency was taken as the objective, it would inevitably lead to a large model-updating error. In this paper, inspired by the image recognition technology, a mode-identification index based on image-similarity recognition (ISR) is proposed to provide a novel approach for mode identification. Model-updating of complex structures may involve hundreds of design variables, which limits the extensive use of traditional gradient optimization methods, since the quantitative relationship between the design variables and the objective function is difficult to be described mathematically.