## 1 Introduction and Literature Review  
Structural health monitoring (SHM) has gained great significance in the last decade due to well-founded signal processing developments, vibration engineering, computational intelligence, etc. Moreover, the SHM methods have gained more attention by the research community due to its role in guaranteeing structure safety and severability [1, 2]. Efficient SHM systems permit precocious detection of structural damage through non-destructive testing, data analysis, and modal feature analysis in order to prevent structural failure [3, 4]. Major SHM methods [5] include, but are not limited to, inverse analysis, model updating, machine learning, probabilistic and deterministic approaches, metaheuristics, etc. Nevertheless, due to the high uncertainty, as well as environmental and loading effects, associated with damage inspection, it is impossible to achieve a \"one fitsall\" method for all SHM applications; in addition to the various different types of civil structures being tested, such as frames, beams, bridges, buildings, dams, etc., or mechanic structures, such as rotary machines, aeronautical structures, trains, vehicles, etc. [6; 7; 8]. Modern damage detection techniques mainly rely on date-driven methods which focus on detecting the existence of damage and identifying the relative location of it. Other modern approaches depend on model-based inverse problems, for which the FE model updating is implemented. FE model updating is conducted to identify damage location and severity by means of finding the correlation between numerical and experimental modal responses. Thereafter, the FE model is altered to match the real situation of the structure. This approach will be the main focus of this work. Vibration modal characteristics are main structural parameters that are utilized for the inverse analysis of structural damage [9; 10]. Such characteristics are mainly categorized into modal frequencies, modal shapes, curvature modal shapes, strain energy, flexibility, kinetic energy, etc. In this regard, researchers often implement single or multiple modal features in their attempts to develop efficient and robust paradigms able to tackle the model-based damage diagnosis problem. The basic idea of solving the inverse problem of model-based damage identification is to construct an optimization problem, in which the error between the measured responses and analytical responses is taken from the FE model [11; 12]. Therefore, by using the modal features [13], the objective function of the inverse problem is developed. An overview of modal-features-based objective functions available in the literature is provided in the following section. The inverse analysis of structural damage identification problems involves two important aspects. The first aspect to be addressed is the formulation of the objective function, which measures the difference between the responses of the FE model of the initial state of the structure and the real responses corresponding to the current situation of the structure experiencing damage [5; 14; 15; 16]. Modal strain energy (MSEn) is one of the most useful modal features that can be used to formulate the objective function. It has proved to be more accurate in detecting minor damage than other modal features, such as natural frequencies and mode shapes [17; 18]. Therefore, the MSEn is utilized as a damage index to detect and localize structural damage. Several studies have employed MSEn damage indicators in bridge structures [19], plates [20], trusses and frames [21], beams [22], etc. Most studies have reported the effective damage identification capabilities of MSEn when applied on different structures, even under conditions that lack complete data or have noisy responses. The second aspect to be addressed, modal kinetic energy (MKEn), is another effective modal characteristic that can be used as an informative indictor of structural damage. Very few research studies have implemented the MKEn for damage identification. For example, [PERSON] et al.