The birth of smart materials such as piezoelectric (PZT) transducers has aided in revolutionizing the field of structural health monitoring (SHM) based on non-destructive testing (NDT) methods. While a relatively new NDT method known as the electromechanical (EMI) technique has been investigated for more than two decades, there are still various problems that must be solved before it is applied to real structures. The technique, which has a significant potential to contribute to the creation of one of the most effective SHM systems, involves the use of a single PZT for exciting and sensing of the host structure. In this paper, studies applied for the past decade related to the EMI technique have been reviewed to understand its trend. In addition, new concepts and ideas proposed by various authors are also surveyed, and the paper concludes with a discussion of the potential directions for future works.

The integration of drones in the construction industry has ushered in a new era of efficiency, accuracy, and safety throughout the various phases of construction projects. This paper presents a comprehensive overview of the applications of drones in the construction industry, focusing on their utilization in the design, construction, and maintenance phases. The differences between the three different types of drones are discussed at the beginning of the paper where the overview of the drone applications in construction industry is then described. Overall, the integration of drones in the construction industry has yielded transformative advancements across all phases of construction projects. As technology continues to advance, drones are expected to play an increasingly critical role in shaping the future of the construction industry.

The continuous monitoring of civil infrastructures is crucial for ensuring public safety and extending the lifespan of structures. In recent years, image-processing-based technologies have emerged as powerful tools for the structural health monitoring (SHM) of civil infrastructures. This review provides a comprehensive overview of the advancements, applications, and challenges associated with image processing in the field of SHM. The discussion encompasses various imaging techniques such as satellite imagery, Light Detection and Ranging (LiDAR), optical cameras, and other non-destructive testing methods. Key topics include the use of image processing for damage detection, crack identification, deformation monitoring, and overall structural assessment. This review explores the integration of artificial intelligence and machine learning techniques with image processing for enhanced automation and accuracy in SHM. By consolidating the current state of image-processing-based technology for SHM, this review aims to show the full potential of image-based approaches for researchers, engineers, and professionals involved in civil engineering, SHM, image processing, and related fields.

Unmanned aerial vehicles (UAVs), commonly known as drones, are a rising topic in remote sensing technologies for structural health monitoring. With technology advancement in cameras, the visual inspection method using drones is gaining much attention in the field of civil engineering. However, although visual inspection methods are feasible for finding cracks in structures, the limitations of image processing for finding internal damage or small defects cannot be ignored. To overcome this problem, a possible application concept of UAV, combined with a vibration-based non-destructive health monitoring method, is proposed. The idea is for the drone to temporarily attach the piezoelectric transducer onto a specific region where excitation and data acquisition occurs simultaneously. This eliminates the need for a structure to be covered with hundreds of sensors for monitoring, as this concept uses a single piezoelectric transducer for monitoring a structure. The proposed work offers new areas of research by converging UAV with a vibration-based method, as associated problems are required to be solved.

Wind energy is one of the most rapidly growing sectors in renewable energy generation, with wind turbines being central to this expansion. Regular maintenance, particularly the inspection of wind turbine blades, is critical to ensure operational efficiency and prevent catastrophic failures. Conventional methods of blade inspection, including ground-based visual inspections, rope-access inspections, and cranes, are time-consuming, expensive, and often hazardous. In recent years, drone-based technologies have emerged as a promising alternative for wind turbine blade inspection. This paper provides a comprehensive review of current drone-based technologies for wind turbine blade inspection, highlighting their advantages, challenges, and future prospects.

In general, the bolted joints that connect and secure components together can be easily spotted in our surroundings. This joining method has been commonly used in various areas of engineering (e.g. aerospace, civil, and mechanical engineering) as it has been proven one of the most effective means to join parts together. Although it has its advantages, the vibration that bolted structures endure during service ultimately causes the bolts to loosen. This can, in turn, have a negative effect on the structure’s safety and may, at worst, cause it to fail. Routine inspections of structures are conducted on a regular basis, with some inspection categories requiring heavy equipment in order to acquire certain data. In addition, monitoring systems can be expensive to install and maintain, especially in large infrastructures. In an effort to rectify this, in this study, a piezoelectric transducer–based nondestructive technique is used in conjunction with the application of a reattachable device to investigate the possibility of creating a low-cost inspection system. The acquired data were processed by means of an artificial neural network technique that showed promising results in terms of mitigating bolt loosening.