Non-destructive techniques play a key role in crack determination and the need for progressive methods for assessing different properties of concrete, as well as for maintenance and monitoring of structures, has led to the development of microwave nondestructive testing techniques (MNDT) for preserving the civil infrastructure. The MNDT technique is an imperative scientific breakthrough that consists of the development of calibration methods, probes, and techniques to detect defects, complex permittivity, moisture leakages, voids, cracks, initial rebar detection, inhomogeneity, etc. in materials [2]. This nondestructive method provides faster determination of the strength of the concrete concerning the destructive methods. Determining epsilon parameters would provide predicting the strength of a concrete sample [12]. Karl Bois et al. proposed that the statistical distribution of multiple reflection property measurements is used as a tool to determine the water-to-cement (w/c) ratio as well as the coarse aggregate volumetric distributions in several concrete mixtures [7]. Electromagnetic waves at microwave frequency range 7–13 GHz using free-space microwave method was used for measuring dielectric properties of concrete. It is observed that both dielectric constant of concrete increase with increasing compressive strength of concrete, but loss factors property of concrete decrease with increasing compressive strength of concrete. Most of the dielectric materials including wood and concrete are nonmagnetic, making the permeability very close to the permeability of free space [10]. An open-ended coaxial probe method is used for the measurement of real and imaginary parts of the complex permittivity of concrete. The increased loss factor at a higher frequency or with higher moisture content, or both, reduces the penetration depth of the wave in concrete, which compromises the benefit of increased detectability [14]. The Electromagnetic wave (EMW) NDT method could be applied to identify the slit orientation on the CFRP composites, while attention must be paid to the evaluation criteria when using various incident angles [15]. R. zoughi et al. proposed that these microwaves and mile millimeter-wave techniques offer certain advantages when detecting fatigue cracks such as the probe may or may not be in contact with the surface under examination, the dimensions of a crack can be estimated, particularly its depth, orientation, edge, and tip locations can be determined [16]. The novel use of microwave measurements is demonstrated to distinguish between mortars containing alkali-silica reactive aggregate and non-reactive aggregate [9]. According to Hamse Abdillahi Haji omer et al. antenna resonance plays a vital role in this concept of structural health monitoring where the S-parameter plot shows the variation of return loss (in dB) over a range of frequencies. The designed antenna resonates at 1.8 and 2.4 GHz respectively [6]. The properties of an open-ended rectangular waveguide probe radiating into Portland cement-based materials at 5GHz (G-band) and 10GHz (X-band) these microwave near-field sensing techniques are also useful for quality control purposes of a mixture in its early stages of curing [8]. Alireza A.chiniforush et al. highlight a lack of precision in the measurement of concrete’s dielectric properties using the commonly used coaxial probe MNDT by comparing the co-axial measurement with those obtained through the two-port technique in the frequency range of 2.6–3.95 GHz [4]. For the answer to the challenge of aging infrastructure, popular terms such as Internet of things and smart structures were coined as a result of the intersection between advances in other engineering disciplines with civil engineering to produce the new field of structural health monitoring (SHM) [5]. As far as microwave nondestructive testing techniques artificial intelligence plays a key role these days in damage detection. Implementing artificial intelligence to classify cracked from non-cracked surfaces has appreciable impacts in terms of sensing sensitivity, cost, and automation. Furthermore, applying artificial intelligence for post-processing the data collected from microwave sensors for handheld test equipment can outperform rack equipment with large screens and sophisticated plotting features [3]. The application of deep architectures inspired by the fields of artificial intelligence and computer vision has made a significant impact on the task of crack detection. In addition to providing higher accuracy in terms of detection, the crack localization is done at the pixel level thereby providing higher localization accuracy [18]. The responses of the multi-crack structure subjected to transit load were determined using the fourth-order Runge-Kutta numerical method and Finite element analysis (FEA) executed using ANSYS software to authenticate the employed numerical method [17]. Artificial Neural Networks, usually called Neural Networks (NNs), are computational models which consist of an interconnected group of artificial neurons. These neurons process information using a connectionist approach to computation. The ANN technique generally provides an efficient tool to classify problems associated with nonlinearities, when they are well represented by input patterns, and to avoid the complexity introduced by conventional computational methods [1]. Having the ability to perform various tasks with outstanding performance, machine learning (ML) has become a popular technique in almost every field. By providing a sufficient amount of data, ML algorithms can automatically digest intrinsic knowledge of the data, such as hidden structures or relationships. Traditional ML techniques require a predefined feature extraction stage to reduce the complexity of the data and make patterns more visible to learning algorithms[19]. An experimental study detailing the evidence related to using an ultrasonic pulse velocity test for accessing the cracking condition between steel and concrete reinforcement is presented along with a multilayer feedforward backpropagation perceptron ANN that is successfully able to predict the crack width thus eradicating the need for simplification assumptions needed to model complex nonlinear stress distribution [11]. This advantage of ANN as an AI model can be employed in NDT to recognize and estimate harmful defects before causing the system failure as well as improve the safety criteria [13].
Much research has been conducted over the past decades on Microwave based Nondestructive testing techniques using various methods with varying frequency ranges. Most of the work was conducted in determining the compressive strength of the concrete for various water-cement ratios using open-ended rectangular and circular waveguide sensors. Artificial intelligence is the recently developing technique in the field of civil structures for Predicting the physical properties, damage detection, mixing ratios, etc., In the present study, Mean squared error analysis was done in PYTHON software.