# Algorithm Development for the Non-Destructive Testing of Structural Damage  
[PERSON]  
1 Centre for Infrastructure Engineering (CIE), Western Sydney University, Kingswood, NSW 2747, Australia 11 Centre for Infrastructure  
The solution requires a classifier that can discriminate structures as either being damaged or intact. Concerning AI techniques, the most commonly used classifiers are based on Discriminant Analysis, Artificial Neural Network, K-Nearest Neighborhood, Support Vector Machine (SVM), Support Vector Regression, Adaptive Neuro-Fuzzy System (ANFIS), Fuzzy Inference System (FIS) and Decision Trees. These algorithms have successfully been utilized to solve engineering problems [3; 4; 5; 6; 13; 14; 15]. The SVM method has been extensively used in many research studies, and it is accepted as an efficient classifier for detecting damage [7; 8; 9]. However, there had been a substantial increase in utilizing advice sets for enhancing SVM performance. However, applying and expressing this knowledge in terms of its constraints still has some difficulties. These techniques require new parameters and operations that can enhance the SVM computational cost. In this regard, the first contribution of this study is to develop non-iterative SVM-based algorithms for analyzing the sensor (smart aggregate) data, which can improve the performance of SVM, rather than traditional SVM. Furthermore, the local structural health monitoring based on microwave and millimeter-wave imaging is an efficient approach for visualizing and localizing detected damage in structures [10; 11; 12; 16; 17; 18]. These methods offer cost-effective methods of local inspection, to identify the exact location of the crack [19; 20]. Microwave- and millimeter imaging techniques are non-contact and one-sided techniques which can be efficiently applied for the non-destructive evaluation of non-transparent materials, as their signals can penetrate the dielectric materials and provide information about the entire structure [21; 22]. In other words, these techniques examine microwave pulses or continuous wave signals, to offer comprehensive descriptions of the systems. In common microwave imaging inspections, the reflection from the test specimen is recorded by the radio frequency (RF) detector, and it is used for the detection of damage inside the specimen [21; 23; 24]. In general, to obtain the desired range resolution and 3D images, relatively complex wide-frequency range measurement systems and image processing algorithms are required. In many applications, wide band techniques may not be applicable, due to specific requirements, such as the depth of penetration in composite materials, and the cost of the techniques. However, there are various microwave- and millimeter-wave imaging systems that have been used for NDT in structures, including relatively simple single-frequency reflectometers [10; 21; 22; 25], and also relatively complex wide-band reflectometers, such as synthetic aperture radar (SAR) [11; 26]. However, most of the generated images have very low resolution, in which the investigating object is not recognizable. Therefore, the proper image processing techniques [27] are required to handle the imaging data. In this regard, the second contribution of this study is to develop algorithms using image processing techniques to create enhancement and segmentation of obtained microwave images of civil structures for the recognition of anomalies, and to obtain the area of interest. In contrast to the results of prior research [22], this study performs a systematic investigation of the problem, and this is followed by various image processing approaches. The methodology of this research relies on modern computer vision techniques, instead of traditional photogrammetric methods. The remainder of the paper is organized as follows: The damage detection technique based on ML is described in Section 2 of this paper. Then, damage detection and visualization, using the microwave- and millimeter-wave imaging systems are presented in detail.