Very early age (0–20 h) concrete hydration is a complicated chemical reaction. During the very early age period, the concrete condition dramatically changes from liquid state to solid state. This paper presents the authors’ recent research on monitoring very early age concrete hydration characterization by using piezoceramic based smart aggregates. The smart aggregate (SA) transducer is designed as a sandwich structure using two marble blocks and a pre-soldered lead zirconate titanate (PZT) patch. Based on the electromechanical property of piezo materials, the PZT patches function as both actuators and sensors. In addition, the marble blocks provide reliable protection to the fragile PZT patch and develop the SA into a robust embedded actuator or sensor in the structure. The active-sensing approach, which involved a pair of smart aggregates with one as an actuator and the other one as a sensor, was applied in this paper’s experimental investigation of concrete hydration characterization monitoring. In order to completely understand the hydration condition of the inhomogeneous, over-cluttering, high-scattering characteristics of concrete (specifically of very early concrete), a swept sine wave and several constant frequency sine waves were chosen and produced by a function generator to excite the embedded actuating smart aggregate. The PZT vibration induced ultrasonic wave propagated through the concrete and was sent to the other smart aggregate sensor. The electrical signal transferred from the smart aggregate sensor was recorded during the test. As the concrete hydration reaction was occurring, the characteristic of the electrical signal continuously changed. This paper describes the successful investigation of the three states (the fluid state, the transition state, and the hardened state) of very early age concrete hydration based on classification of the received electrical signal. Specifically, the amplitude and frequency response of the electrical signal were of main interest. Both the swept sine wave and the constant frequency sine wave excitation methods presented the same conclusion on the three concrete states during the hydration, which enhances the reliability of the active-sensing approach for very early age concrete hydration monitoring.

Aimed at monitoring bolt connection status and detecting bolt loosening, a piezoceramic based active sensing method is developed in this article. Two piezoceramic patches are bonded to the two different parts of a bolted connection. In the active sensing approach, one piezoceramic patch is used as an actuator to generate an ultrasonic wave, and the other one is used to detect the wave that propagates through the bolted connection. The received energy depends on the interface fastening pressure which is determined by the bolt torque. By analyzing the received energy, the status of bolt connection and bolt loosening can be detected. To study the effectiveness of the proposed method, an experiment apparatus is set up, and the experimental results show that the wave energy propagated across the interface is proportional to the torque level that is used to pretension the bolt. This experimental study demonstrates the potential of the piezoceramic based active sensing method for the monitoring of bolt loosening status.

Bolted joints are among the most common building blocks used across different types of structures, and are often the key components that sew all other structural parts together. Monitoring and assessment of looseness in bolted structures is one of the most attractive topics in mechanical, aerospace, and civil engineering. This paper presents a new percussion-based non-destructive approach to determine the health condition of bolted joints with the help of machine learning. The proposed method is very similar to the percussive diagnostic techniques used in clinical examinations to diagnose the health of patients. Due to the different interfacial properties among the bolts, nuts and the host structure, bolted joints can generate unique sounds when it is excited by impacts, such as from tapping. Power spectrum density, as a signal feature, was used to recognize and classify recorded tapping data. A machine learning model using the decision tree method was employed to identify the bolt looseness level. Experiments demonstrated that the newly proposed method for bolt looseness detection is very easy to implement by ‘listening to tapping’ and the monitoring accuracy is very high. With the rapid in robotics, the proposed approach has great potential to be implemented with intimately weaving robotics and machine learning to produce a cyber-physical system that can automatically inspect and determine the health of a structure.

Bolted joint are among the key components that enable the robust assembly of a wide variety of structures. However, due to wear and tear over time, bolted joint may loosen, and if not detected in its early stages, can lead to devastating results. A monitoring method that can detect bolted joint looseness prior to bolt failure will be essential for the continued operation of the host structure and depending on the situation, the safety of the occupants. Prior research has proven the electromechanical impedance method (EMI) to be an effective technique for detecting the loosening of bolted joints, however, EMI-based methods until now are focused on qualitative health monitoring, which can only provide limited information about the damage. Thus, this paper attempts to quantify EMI based methods through the integration of fractal contact theory, the result of which is a novel electromechanical impedance model for quantitative monitoring of bolted looseness. The method determines the effective impedance of the bolted joint and is applied to develop the relationship between the electrical impedance of a piezoceramic patch installed on the joint and the mechanical impedance of the bolted joint. The mechanical impedance of the bolted joint under various preloads is computed by using the fractal contact theory. Then, the bolted looseness can be monitored quantitatively. At last, a set of verification tests under different applied preload of bolted joint are conducted to verify the validity of the proposed model in this paper.

Unmanned aerial vehicle (UAV) technologies combined with digital image processing have been applied to the crack inspection of bridge structures to overcome the drawbacks of manual visual inspection. However, because of environmental interference such as uneven natural light, noises produced by the UAV hardware, spots on the road surface, and UAV jitter, the collected images by UAVs are usually fuzzy and have relatively low contrast. In the processing of such collected images the traditional edge detection algorithms such as Canny algorithm, Prewitt algorithm, and Sobel algorithm have low detection accuracy because of their poor antinoise ability. K-means clustering method is one of the unsupervised learning methods. Nevertheless, in the case of a small amount of images, it cannot achieve the accurate identification of the cracks from the collected image. In this paper, a new crack detection method based on the crack central point, namely crack central point method (CCPM), is proposed to address these essential issues. With a small amount of images, the new method can quickly and accurately identify the cracks in the collected images. Compared with the traditional edge detection methods and K-means clustering method, the CCPM method has better adaptability and robustness.

Monitoring of bolt looseness is essential for ensuring the safety and reliability of equipment and structures with bolted connections. It is well known that tangential damping has an important influence on energy dissipation during wave propagation across the bolted joints, which require different levels of preload. In this paper, the energy dissipation generated by tangential damping of the bolted joints under different bolt preloads was modeled analytically based on fractal contact theory, which took the imperfect interface into account. A saturation value exists with the increase of the bolt preload, and the center frequency of emitted signal is demonstrated to affect the received energy significantly. Compared with previous similar studies based on experimental techniques and numerical method, the investigation presented in this paper explains the phenomenon from the inherent mechanism, and achieves the accurate quantitative monitoring of bolt looseness directly, rather than an indirect failure index. Finally, the validity of the proposed method in this paper was demonstrated with an experimental study of a bolted joint with different preload levels.