## 1 Introduction  
According to international norms and regulations, any building materials, products, and elements should have specific physical and strength properties to ensure that the required ultimate and serviceability limit states are met in designed buildings over their whole useful lives [1]. Therefore, there is paramount interest in the constant monitoring of structures for the lifetime assessment of industrial structures [2]. Due to the beneficial characteristics of non-destructive testing (NDT), including the non-destructive nature, repeatability, and economical aspect, it is widely applied in civil engineering, although a disadvantage of this is that the NDT methods are indirect methods. NDT methods can carry out a condition assessment to provide information for the structural performance of the material, including equipment integrity analysis, the corrosion monitoring of structures and equipment, corrosion damage evaluation, fatigue and creep damage prediction, and fitness-for-service evaluations [2]. Several conventional NDT methods are mainly used in routine services to detect the integrity of industrial components [2]. Some methods require expertise and have their limitations. For instance, in the radiography testing method, careful attention needs to be paid to operator safety [3], while eddy current testing only works for conducting materials and requires a highly skillful and experienced operator [2]. On the contrary, the ultrasonic method is widely employed for wall thickness inspections due to its advantages of [4, 5]; being nonhazardous and applicable for thickness measurements of thick material, the detection of discontinuity, and the determination of material properties. However, the ultrasonic thickness measurement (UTM) is based on measuring the transit time that is required for a short ultrasonic pulse to travel through the material once, twice, or several times, which leads to the need for the UTM system to know the precise physical characteristics, such as the ultrasonic velocity, going through the material [6]. Therefore, often authors investigate and develop new methods for determining ultrasonic velocity [7; 8], but usually they present measurement results without the associated measurement uncertainty. Moreover, in [9], the author pointed out that, in ultrasonic inspections, the human factors of time pressure, mental workload, and experience influence the quality of the performance, which can bring about inaccurate inspection results. On the other hand, an electromagnetic (EM) wave has been involved in the thickness inspection of non-metallic materials due to its remarkable features. It does not require a couplant for signal transmission, has a low power consumption, and has a relatively inexpensive price. The material thickness was measured [10; 11] by resonant near-field probes whose resonant frequency highly depends on the effective permittivity. This factor will be greatly influenced by the much thicker structure respective to the application discussed herein. Furthermore, it will be very difficult to relate a value (i.e., the resonant frequency) representing the effective permittivity to the liner thickness in the presence of other variables (i.e., wall thickness). In [12; 13], a method employing an open-ended waveguide probe radiating into a dielectric structure was proposed for thickness estimation. The slight air gap between the waveguide aperture/flange and the structural wall during measurement may have caused the inaccurate results. Consequently, it leads to the need for reducing the impacts of human factors in measurements as well as the need for skillful inspectors with years of training. Recently, the machine learning (ML) algorithms and computational tools (GPU acceleration) have been improved significantly and enabled more powerful models that reach the human level in many difficult tasks including image classification and machine translation, and NDT is not an exception. ML systems can be used to automate systems, where a direct algorithmic description is intractable.