Structural health monitoring is an important aspect of maintenance for bridge columns in areas of high seismic activity. In this project, recently developed piezoceramic-based transducers, known as smart aggregates (SA), were utilized to perform structural health monitoring of a reinforced concrete (RC) bridge column subjected to pseudo-dynamic loading. The SA-based approach has been previously verified for static and dynamic loading but never for pseudo-dynamic loading. Based on the developed SAs, an active-sensing approach was developed to perform real-time health status evaluation of the RC column during the loading procedure. The existence of cracks attenuated the stress wave transmission energy during the loading procedure and reduced the amplitudes of the signal received by SA sensors. To detect the crack evolution and evaluate the damage severity, a wavelet packet-based structural damage index was developed. Experimental results verified the effectiveness of the SAs in structural health monitoring of the RC column under pseudo-dynamic loading. In addition to monitoring the general severity of the damage, the local structural damage indices show potential to report the cyclic crack open-close phenomenon subjected to the pseudo-dynamic loading.

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.

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.

Reinforced concrete underground pipelines are some of the most widely used types of structures in water transportation systems. Cracks and leakage are the leading causes of pipeline structural failures which directly results in economic losses and environmental hazards. In this paper, the authors propose a piezoceramic based active sensing approach to detect the cracks and the further leakage of concrete pipelines. Due to the piezoelectric properties, piezoceramic material can be utilized as both the actuator and the sensor in the active sensing approach. The piezoceramic patch, which is sandwiched between protective materials called ‘smart aggregates,’ can be safely embedded into concrete structures. Circumferential and axial cracks were investigated. A wavelet packet-based energy analysis was developed to distinguish the type of crack and determine the further leakage based on different stress wave energy attenuation propagated through the cracks.

In this paper, a novel percussion-based bolt looseness monitoring approach using intrinsic multiscale entropy analysis and back propagation (BP) neural network is proposed. The percussion-caused audio signals of bolt connection are decomposed by complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to obtain intrinsic mode functions (IMFs). The IMFs are in order of high-to-low instantaneous frequencies and contain underlying dynamical characteristics of audio signals. Multiscale sample entropy (MSE) is improved by smoothed coarse graining process, and the proposed improved multiscale sample entropy (IMSE) values of certain IMFs are adopted as condition indicators in bolt looseness monitoring. The intrinsic multiscale entropy analysis consisting of CEEDMAN and IMSE extracts underlying dynamical characteristics during percussion-caused audio signal processing to identify bolt looseness conditions. The condition indicators, namely IMSE values at smallest scale factors, are employed as input of BP neural network for training and testing, to achieve accurate and stable bolt looseness condition monitoring. The effectiveness and superiority of the proposed approach have been validated by theoretical derivation and practical experimental researches, and the adaptivity and robustness of the proposed approach are also illustrated. The results of the research in this paper demonstrate the proposed approach is promising in practical applications of bolt looseness monitoring.