Sensitivity of Ultrasonic Coda Wave Interferometry to Material Damage-Observations from a Virtual Concrete Lab

Ultrasonic Coda Wave Experiment and Simulation of Concrete Damage Process under Uniaxial Compression

Using the coda wave interferometry (CWI) method to obtain the ultrasonic coda wave characteristics of loaded concrete is an important method to evaluate the mechanical response of concrete. In this paper, the ultrasonic coda wave characteristics of C40–C70 concrete specimens (four strengths of concrete) under uniaxial compression were tested by laboratory experiments. Furthermore, to clarify the relationship between the internal damage process of concrete and the change rate of coda wave velocity, an ultrasonic coda wave discrete element simulation model combined with digital image processing technology was established. The results show that the coda wave is very sensitive to small changes in the compressive damage to concrete, and the change in coda wave velocity can correspond to the development process of concrete damage. This research is conducive to a better understanding of the complex material behavior of compressive concrete and proves the feasibility of ultrasonic field simulation and processing by using numerical simulation images of concrete damage.

Feasibility of Using Shear Wave Ultrasonic Probes as Pump-Wave Sources in Concrete Microcrack Detection and Monitoring by Nonlinear Ultrasonic Coda Wave Interferometry

This paper represents a first attempt to study the feasibility of using shear wave (SW) ultrasonic probes as pump-wave sources in concrete microcrack detection and monitoring by Nonlinear Ultrasonic Coda Wave Interferometry (NCWI). The premise behind our study is that the nonlinear elastic hysteretic behavior at microcracks may depend on their orientation with respect to the stationary wave-field induced by the pump-wave source. In this context, the use of a SW probe as a pump-wave source may induce the nonlinear elastic behavior of microcracks oriented in directions not typically detected by a conventional longitudinal pump-wave source. To date, this premise is hard to address by current experimental and numerical methods, however, the feasibility of using SW probes as a pump-wave source can be experimentally tested. This idea is the main focus of the present work. Under laboratory conditions, we exploit the high sensitivity of the CWI technique to capture the transient weakening behaviour induced by the SW pump-wave source in concrete samples subjected to loading and unloading cycles. Our results show that after reaching a load level of 40% of the ultimate stress, the material weakening increases as a consequence of microcrack proliferation, which is consistent with previous studies. Despite the lack of exhaustive experimental studies, we believe that our work is the first step in the formulation of strategies that involve an appropriate selection and placement of pump-wave sources to improve the NCWI technique. These improvements may be relevant to convert the NCWI technique into a more suitable non-destructive testing technique for the inspection of microcracking evolution in concrete structures and the assessment of their structural integrity.

Percussion-Based Pipeline Ponding Detection Using a Convolutional Neural Network

Pipeline transportation is the main method for long-distance gas transportation; however, ponding in the pipeline can affect transportation efficiency and even cause corrosion to the pipeline in some cases. A non-destructive method to detect pipeline ponding using percussion acoustic signals and a convolution neural network (CNN) is proposed in this paper. During the process of detection, a constant energy spring impact hammer is used to apply an impact on the pipeline, and the percussive acoustic signals are collected. A Mel spectrogram is used to extract the acoustic feature of the percussive acoustic signal with different ponding volumes in the pipeline. The Mel spectrogram is transferred to the input layer of the CNN and the convolutional kernel matrix of the CNN realizes the recognition of pipeline ponding volume. The recognition results show that the CNN can identify the amount of pipeline ponding with the percussive acoustic signals, which use the Mel spectrogram as the acoustic feature. Compared with the support vector machine (SVM) model and the decision tree model, the CNN model has better recognition performance. Therefore, the percussion-based pipeline ponding detection using the convolutional neural network method proposed in this paper has high application potential.