# Recent Advancements in Non-Destructive Testing Techniques for Structural Health Monitoring
## 3 Ground Penetrating Radar  
Ground-penetrating radar (GPR) is one of the most used electromagnetic (EM) wave-based techniques in civil engineering owing to the noninvasive, low costs, and rapid monitoring nature of the technique. GPR is used for the evaluation of the stability of concrete and masonry structures, maintenance of the structures, and structural health monitoring of infrastructure conservation. In addition, the GPR technique is investigated to inspect the asphalt pavements in order to detect voids, fractures, or other types of defects [21; 22]. The working principle of the GPR technique is the use of the microwave region of the EM spectrum that employs a transmitter and a receiver (antennas) to send and capture the reflected wave (as shown in Figure 3. Radiated short pulses of high-frequency EM energy are generated by pulse mode GPR systems that penetrate through an object via a transmitting antenna and then detected by a receiver. Various frequencies are used depending on the requirement of the measurement, namely resolution versus penetration depth, i.e., the higher the operating frequency, the higher the resolution, and the lower the penetration depth of the system. To maintain the accuracy of the GPR systems, they must be calibrated with a core sample of the tested material [4]. The measured signal is amplified, processed, and analyzed based on the altered signal, i.e., the transmitted energy is partially absorbed and transmitted by the material under investigation, and the rest is reflected and captured by the system. The changes in the reflected energy, namely, electrical conductivity, dielectric permittivity, and magnetic permeability, help to detect, for instance, cables, pipes, rods inside structures [23] and to monitor the corrosion in reinforced concrete (RC) structures [24]. GPR inspections are often carried out in combination with other noninvasive geophysical techniques, namely, acoustic techniques, infrared thermography (IRT) and, electrical resistivity tomography (ERT) [21]. In the past five years, researchers focused on the development of the radar, and the development of the radar, and the development of the radar, and the development of the radar, and the development of the radar, and the development of the radar, and the development of the radar, and the development of the radar, and the development of the radar, respectively. Figure 3: Ground Penetrating Radar principle of work. opment of various GPR data analysis methods in order to overcome the limitations of the technique. One of the main drawbacks of the technique is the requirement for a highly skilled specialist to interpret captured data from the system. Therefore, researchers have been investigating various approaches to address the limitation, namely the implementation of artificial intelligence techniques to analyze the data for the detection of objects. Typically, there are three types of data obtained via the GPR system, namely A-, B- and C-scan data. A-scan data is punctual time-domain data (z-axis) at a certain spatial point, while the B-scan provides x-z domain data (x-axis) that are generated from numerous A-scan data along the scanning route. The C-scan data is obtained from the x-y domain data by combining B-scan images, which is acquired utilizing multiple antennas [25]. [PERSON] et al. [26] proposed the three-dimensional convolutional neural network to analyze the GPR data in order to classify the underground objects. The 13 km of the 3D data was collected from urban roads in Seoul, South Korea, using a multichannel antenna mounted on the vehicle. The data consisted of various objects, namely, cavities, pipes, manholes and subsoil background. The proposed classification demonstrated the accuracy for cavities, pipes, manholes, and subsoil backgrounds 88%, 100%, 100% and 100%, respectively. The average classification accuracy for four underground objects is 97%. Researchers are exploring other methods to improve GPR data assessment. For instance, [PERSON] et al.