## 1 Introduction  
Health monitoring is a critically important issue, not only for people, but also for civil infrastructures throughout their lifetime. Bridges, railways, tunnels, roads, and the various buildings distributed across the land play a crucial role in economic growth [1, 2] and the public life of cities [3]. Structural deterioration is one of the major long-term concerns regarding civil infrastructure, as it can become a potential and latent risk factor for human safety over time. Indeed, the wear and tear of materials due to weathering, continuous solicitations, or unforeseen events (such as natural disasters) can progressively compromise the integrity of a structure to the point of sudden collapse or loss of functionality, with severe consequences [4]. Therefore, periodic integrity inspections of civil infrastructure are crucial in their ensuring safety and total efficiency. On-site and manual visual inspections were the conventional method to detect damage and structural alterations in civil infrastructure in the past. However, on-site visual inspections conducted by experienced examiners are often impractical, time-consuming, and laborious, especially in the case of large-scale structures, such as bridges and buildings [5, 6]. Moreover, in many cases, the assessment results largely depend on the inspectors' personal competence and subjective evaluation [7]. To overcome the difficulties and limitations of manual visual inspections, structural health monitoring (SHM) has received significant attention over the last two decades as a powerful emerging diagnostic tool for evaluating structural integrity due to advances in technology. The evidence of this growing interest was objectively demonstrated in reference [8]. An in-depth analysis of articles dealing with SHM topics, published in three major journals during 2005-2019, showed a progressive and significant upsurge in the number of articles published, increasing from 22 publications in 2005 to 495 in 2019 (increase factor: 22.5), with a significant increase in the number of authors involved in this research as well (increase factor: 28). The primary aim of SHM is to collect objective measurements and information related to specific structural properties to trigger timely maintenance interventions and prevent serious consequences. In practice, an SHM system includes sensors, data acquisition modules, along with algorithms for signal processing, structural diagnosis, and damage detection [9]. In recent years, different types of sensors and techniques have been proposed for SHM. Traditional approaches involved the use of contact-based sensors applied directly to the structure under investigation: these include accelerometers, strain gauges, fiber-optic sensors, piezoelectric sensors, ultrasonic waves, displacement sensors, and others, as reported in reference [10]. However, these wired and wireless sensors are often impractical for installation and maintenance on large-scale structures and typically provide only scattered measurements related to application points. In the past few years, SHM approaches have shifted from conventional contact-based solutions to more efficient and practical non-contact sensors, partially due to recent advances in innovative technologies. In particular, optical sensors, drones, robots, and smartphones, combined with artificial intelligence (AI), such as deep learning models, machine learning methods, data mining techniques, and data-fusion/sensor-fusion approaches, are attracting growing interest, overcoming the practical limitations of contact-based sensors and fostering a paradigm shift in the context of SHM [11, 12, 13, 14, 15]. The in-depth analysis in [8] confirms the existing paradigm shift related to the change in supporting sensors.