## 1 Introduction  
Damage diagnosis and analysis play a pivotal role within the broader field of structural engineering. This process is instrumental in averting catastrophic failures. Damage diagnosis mainly consists of three steps: detection, isolation, and identification. While an anomaly can be investigated during the detection stage, efforts are focused on pinpointing the types of the fault (locations and severity included) along with the damaged component in the isolation and identification phases, respectively [1]. Following these steps, the remaining useful life of a structure can be estimated. Damage diagnosis is particularly valuable when applied to large, complex and costly engineering structures, especially those made of fiber-reinforced (FRP) composites as they exhibit heightened sensitivity to Low-Velocity Impacts (LVIs) [2,3]. LVIs could induce Barely Visible Damages (BVD) such as delamination, as well as fiber failures, and matrix cracking that can significantly diminish the residual strength of the structure [4, 5, 6]. Notably, LVI damage typically found its maximum extension at the surface opposite to the impacted one; this aspect complicates the malfunction identification process with various challenges. Furthermore, structural changes could also result from the environmental and operational conditions (EOCs), naturally affecting the components during their life cycle: factors such as temperature [7] and load variations [8] can lead to fatigue crack onset and propagation [9, 10]. The pressing need to enhance safety and reliability while simultaneously reducing design and maintenance costs is driving advancements in data collection, processing methods, and computing power. One of the promising remedies to the issue is structural health monitoring (SHM). SHM facilitates the establishment of a detection and tracking strategy for damage progression to estimate the structural residual life span. This, in turn, enables the implementation of condition-based maintenance policies by collecting, interpreting, and analyzing data gathered by opportunely distributed sensors embedded or attached to the structures [11; 12]. Within the domain of SHM, a range of methods can be employed to enhance the continuous monitoring of structural health. These methods encompass eddy current, vibration analysis, acoustic emission analysis, electrical resistance measurements, impedance analysis, infrared thermography, and damping assessment [10]. Moreover, the choice of the appropriate sensor type is a contentious issue. Various sensors, including the acoustic emission (AE) sensors, Eddy-current transducers, accelerometers, laser interferometers, fiber-optic sensors, and piezoelectric lead zirconate titanate (PZT) elements, have demonstrated their utility in structural health monitoring. Notably, many of these sensors operate in a passive manner. However, PZT sensors can be used as active actuators as well, in terms of dual piezoelectric effect. Compact size, low weight, minimal power consumption, and the ability to generate the wave signal with the desired amplitude, frequency, and waveform are the main advantages offered by PZT elements, making them a preferred choice for SHM. Beyond all these merits, SHM based on ultrasonic guided waves (UGW) stands out as one of the most accurate techniques that can be used in a variety of plate-like structures, spanning from airplanes to medical composites, for malfunction diagnosis [13], because of their powerful capability of long-distance propagation with high speed and little loss of energy. UGWs are dependent on the properties and thickness of the medium they propagate through. Cost-effectiveness, ability to monitor large areas (guided waves can propagate with little attenuation in thin-walled structures), and high sensitivity to detect small damages are all benefits of SHM based on UGW and PZT elements [14]. UGWs signals are generated and received by the PZTs across the monitored area, so variations in the signals are then analyzed to diagnose damage in the structure [15].