###### Abstract
The electro-mechanical impedance (EMI) method is a local SHM technique. Based on a literature review, it is evident that the EMI method has shown considerable promise in promptly detecting damage in RC structural members. Several recent studies have demonstrated the utility of the EMI method in detecting structural damage associated with concrete cracking [19; 20], steel reinforcement yielding [21], and force quantification [22; 23]. The implementation of the EMI method relies significantly on the utilization of smart piezoelectric materials, particularly the piezoelectric lead zirconate titanate (PZT) patches. These materials offer several advantageous qualities for SHM applications, including low energy consumption, cost-effectiveness, ease of application, compact size, portability, and the dual functionality of serving as both sensors and actuators [24]. The EMI method leverages the inherent electro-mechanical coupling of PZTs to identify anomalies in the structural integrity of the host structure [25]. A substantial body of existing research within the broader literature has focused on assessing the effectiveness of EMI-based monitoring of concrete, particularly concerning various causes of degradation. These causes include but are not limited to the corrosion of rebars in RC members [17; 26; 27; 28], the impact of heating time [29; 30], and the loss of concrete mass [31; 32; 33]. Moreover, the EMI method has been utilized to assess the tendon duct grouting compactness [34; 35] and the setting time of fiber-reinforced concrete [36; 37]. Additionally, the EMI method has been widely utilized in studies to evaluate damage induced by the loading in concrete specimens, with particular attention given to flexural-induced damage in RC and fiber-reinforced concrete beams [22; 38; 39; 40; 41] as well as standard concrete specimens under compression loading [20; 23; 42; 43; 44; 45]. Furthermore, research efforts have extended to examining the behaviour of real-scale members of RC structures, including beams [46; 47; 48; 49; 50] and joints [51; 52; 53; 54]. The EMI method has also been employed to promptly detect FRP debonding of RC members [48; 50]. Recently, researchers have shown a growing interest in the practical and reliable SHM of retrofitted RC members. Therefore, the literature has investigated the efficacy of various non-destructive evaluation techniques to monitor their seismic performance [5; 49; 52; 55]. Usually, C-FRP retrofitting scheme failures are caused by premature debonding or fracture, leading to a brittle failure of the RC member [47]. Therefore, promptly identifying any performance degradation in retrofitted RC members is imperative. The nature of lower-order modal parameters makes global vibration-based approaches insensitive for prompt damage identification [5]. Local monitoring techniques, such as acoustic emission and EMI, demonstrate a high sensitivity in the early detection of damage formation [56; 57]. Additionally, high-frequency excitation signals used by the EMI method amplify its sensitivity to damage. Hence, any minor impairment within the scanning area of a PZT transducer will be reflected in its EMI responses [21]. Previous studies have primarily relied on statistical analysis using scalar indices. Scalar indices quantify the variation of EMI responses captured in different structural conditions. Statistical damage indices have shown consistent performance across various scenarios in the literature [20; 46; 53; 58]. However, there is growing interest in exploring the potential of integrating machine learning algorithms into this field through ongoing research. The primary advantage of utilizing machine learning over scalar indices stems from the scalar nature of statistical indices. In addition, assessing the retrofitting performance of RC members is a challenging endeavor owing to the intricate nature of the material and the influence of various phenomena acting independently, in conjunction, or in succession.