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Zero-Group-Velocity (ZGV) guided mode has recently received particular attention because of its highly sensitive to structural changes, such as defects. Parallel to ZGV, in the present work, we introduce a new guided mode so-called Minimum-Group-Velocity (MGV) that is a remarkable phenomenon in defect detection applications. Strictly speaking, when any two phase-velocity dispersion curves of Lamb modes approach each other without overlapping, we observe this new mode in group-velocity dispersion curves. It is worth noting that this MGV mode has never been addressed in literature. We compute the Lamb-like modes in asymmetric viscoelastic sandwich plates using the Stiffness Matrix Method (SMM). We show that the change in properties of this sandwich can dramatically affect frequency ranges associated with ZGV and MGV modes. However, a linear correlation between phase velocities and this change on viscoelastic properties is discussed, where we observed an interconnection phenomenon in the dispersion curves. This interconnection happens between specific points on the dispersion curves, each corresponding to a ZGV- or MGV-mode. On the other hand, we verify that the detection of changes in level of viscoelastic properties or variations in the thickness of the buffer viscoelastic layer is possible within a limited segment of the damped mode curve. [ABSTRACT FROM AUTHOR]

Monitoring damage in advanced composite structures proves challenging due to insufficient transfer and reuse of diagnostic models. Such limitations fail to meet practical monitoring requirements. This paper proposes an innovative method to monitoring damage via Lamb waves based on transfer learning. Firstly, the proposed method utilizes algorithm-centric transfer learning to extract transferable features from the source domain and a limited amount of data from the target domain, using the domain adaptive feature mapping. The aim of this method is to achieve feature adaptation in both the source and target domains by obtaining approximate distribution patterns in the feature space. Subsequently, a fine-tuning method is presented based on data-centric transfer learning for adaptive damage identification. A screening mechanism utilizing a coarse-to-fine strategy is employed to select suitable samples for diagnosing and evaluating damage in the target domain. The proposed method is verified by targeted monitoring cases of advanced composite structures under four monitoring stages, namely, detection of the damage existence, identification of damage types, damage localization and quantification. The results show that the proposed method can present higher monitoring accuracy in general damages for advanced composite structures than nine state-of-the-art methods. [ABSTRACT FROM AUTHOR]