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.

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.

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.

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.

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Concrete-encased composite structure exhibits improved strength, ductility and fire resistance compared to traditional reinforced concrete, by incorporating the advantages of both steel and concrete materials. A major drawback of this type of structure is the bond slip introduced between steel and concrete, which directly reduces the load capacity of the structure. In this paper, an active sensing approach using shear waves to provide monitoring and early warning of the development of bond slip in the concrete-encased composite structure is proposed. A specimen of concrete-encased composite structure was investigated. In this active sensing approach, shear mode smart aggregates (SAs) embedded in the concrete act as actuators and generate desired shear stress waves. Distributed piezoceramic transducers installed in the cavities of steel plates act as sensors and detect the wave response from shear mode SAs. Bond slip acts as a form of stress relief and attenuates the wave propagation energy. Experimental results from the time domain analysis clearly indicate that the amplitudes of received signal by lead zirconate titanate sensors decreased when bond slip occurred. In addition, a wavelet packet-based analysis was developed to compute the received signal energy values, which can be used to determine the initiation and development of bond slip in concrete-encased composite structure. In order to establish the validity of the proposed method, a 3D finite element analysis of the concrete-steel bond model is further performed with the aid of the commercial finite element package, Abaqus, and the numerical results are compared with the results obtained in experimental study.

Recently developed piezoceramic-based transducers, known as smart aggregates (SAs), have shown their applicability and versatility in various applications of structural health monitoring (SHM). The lead zirconate titanate (PZT) patches embedded inside SAs have different modes that are more suitable for generating or receiving different types of stress waves (e.g. P and S waves, each of which has a unique role in SHM). However, due to the geometry of the 2D PZT patch, the embedded SA can only generate or receive the stress wave in a single direction and thus greatly limits its applications. This paper is the first of a series of two companion papers that introduces the authors’ latest work in developing a novel, embeddable spherical smart aggregate (SSA) for the health monitoring of concrete structures. In addition to the 1D guided wave produced by SA, the SSA embedded in concrete structures can generate or receive omni-directional stress waves that can significantly improve the detection aperture and provide additional functionalities in SHM. In the first paper (Part I), the detailed fabrication procedures with the help of 3D printing technology and electrical characterization of the proposed SSA is presented. The natural frequencies of the SSA were experimentally obtained and further compared with the numerical results. In addition, the influence of the components’ thickness (spherical piezoceramic shell and epoxy) and outer radius (spherical piezoceramic shell and protection concrete) on the natural frequencies of the SSA were analytically studied. The results will help elucidate the key parameters that determine the natural frequencies of the SSA. The natural frequencies of the SSA can thus be designed for suitability in the damage detection of concrete structures. In the second paper (Part II), further numerical and experimental verifications on the performance of the proposed SSA in concrete structures will be discussed.

Bolted joints are ubiquitous structural elements, and form critical connections in mechanical and civil structures. As such, loosened bolted joints may lead to catastrophic failures of these structures, thus inspiring a growing interest in monitoring of bolted joints. A novel energy based wave method is proposed in this study to monitor the axial load of bolted joint connections. In this method, the time reversal technique was used to focus the energy of a piezoelectric (PZT)generated ultrasound wave from one side of the interface to be measured as a signal peak by another PZT transducer on the other side of the interface. A tightness index (TI) was defined and used to correlate the peak amplitude to the bolt axial load. The TI bypasses the need for more complex signal processing required in other energy-based methods. A coupled, electromechanical analysis with elasto-plastic finite element method was used to simulate and analyze the PZT based ultrasonic wave propagation through the interface of two steel plates connected by a single nut and bolt connection. Numerical results, backed by experimental results from testing on a bolted connection between two steel plates, revealed that the peak amplitude of the focused signal increases as the bolt preload (torque level) increases due to the enlarging true contact area of the steel plates. The amplitude of the focused peak saturates and the TI reaches unity as the bolt axial load reaches a threshold value. These conditions are associated with the maximum possible true contact area between the surfaces of the bolted connection.