“…Considerable efforts have been made to study and improve the EMI technique to overcome such limitation. On one hand, combinations of using statistical damage indicators of impedance responses and other pattern recognition algorithms, such as Artificial Neural Networks (ANNs) (He et al, 2014; Lopes et al, 2000; Selva et al, 2013), Probabilistic Neural Network (PNN) (Na and Lee, 2013), clustering analysis (Langone et al, 2017; Palomino et al, 2012), etc., have been investigated to improve the performance of EMI based damage identification methods. On the other hand, studies on using EMI based damage detection methods and model updating have been carried out to overcome the limitation of classifying phenomenological characterizations with statistical indicators (Cao et al, 2018; Fan et al, 2018b; Shuai et al, 2017; Wang and Tang, 2009), since the aforementioned neural network based EMI methods still cannot relate the changes of impedance responses to changes in structural physical properties, such as stiffness, mass or damping [27].…”