Ultrasonic waves generated by smart aggregates for concealed crack detection in asphalt mixture

Due to the advantages of low power consumption, low price, good reliability, high sensitivity and a large measurement range, smart aggregates (SAs) have been widely used in structural health monitoring. In addition, the use of ultrasonic for asphalt mixture performance evaluation has been studied, but monitoring and evaluating asphalt mixtures based on smart aggregates and ultrasonic has rarely been reported. In this manuscript, smart aggregate-based active sensing technology was used to monitor concealed cracks in asphalt mixtures. Smart aggregates were used as actuators and sensors and were embedded in asphalt mixture beam specimens. Three-point bending tests were carried out to create concealed cracks. A dial gauge and camera were used to measure the width and area of the cracks, respectively. During the experiment, one embedded smart aggregate was used as an actuator to generate sweep frequency waves, and the other smart aggregate was used as a sensor to detect propagating waves. Wavelet packet energy analysis and Short-time Fourier Transform were used to analyze the ultrasonic signals. The effects of crack damage degree and signal frequency on ultrasonic wave propagation in asphalt mixture were studied. Furthermore, a damage index (DI) evaluation method based on ultrasonic energy attenuation was proposed. The results are as follows: Due to the viscoelastic property of the asphalt mixture, the attenuation of low-frequency ultrasonic waves was much smaller than that of high-frequency ultrasonic waves. The higher the ultrasonic frequency was, the more sensitive the detection of concealed cracks. However, when the ultrasonic frequency was greater than 160 kHz, it rapidly attenuated in the asphalt mixture. With increasing crack width, the ultrasonic energy decreases, and the damage index (DI) increases continuously. The experimental results show that the combination of smart aggregate and the damage index based on signal energy can be used to effectively identify the existence and severity of hidden cracks in asphalt mixtures. It also has the potential to predict the development of concealed cracks in asphalt pavement. It can be used for real-time health monitoring and preventive maintenance of asphalt pavement.