In this study, we investigate a millimeter wave (mmWave) synthetic aperture radar (SAR) imaging scheme utilizing a low-cost frequency modulated continuous wave (FMCW) radar to take part in non-destructive testing which could be a useful tool for both civilian and military demands. The FMCW radar working in the frequency range from 76 GHz to 81 GHz is equipped with a 2-D moving platform aiming to reconstruct the 2-D image of the shape of the target object. Due to the lab environment containing several devices and furniture, various noise and interference signals from the floor are not avoidable. Therefore, the digital signal processing algorithms are joined to remove the undesired signals as well as improve the target recognition. This study adopts the range migration algorithms (RMAs) on the processed reflected signal data to form the image of the target because of its verified ability in this type of mission. On the other hand, the integration of compressed sensing (CS) algorithms into the SAR imaging system is also researched which helps to improve the performance of the system by reducing the measurement duration while still maintaining the image quality. Three minimization algorithms are used involving the imaging system as the CS solvers reconstruct the radar data before being processed by RMA to form the image. The proposed imaging scheme demonstrates its good ability with high azimuth resolution in the mission of detecting tiny cracks in the rebar of reinforced concrete. In addition, the participation of CS algorithms improves the performance of the scheme as the cracks on the rebar can be located on the images, which are reconstructed from only 30% of the dataset. The comparison of CS solvers shows that ADMM outperforms the other candidates in the reconstruction task.

Wall-thinning in building structures due to corrosion and surface erosion occurs due to the severe operating conditions and the changing of the surrounding environment, or it can result from poor workmanship and a lack of systematic monitoring during construction. Hence, the continuous monitoring of structures plays an important role in decreasing unexpected accidents. In this paper, a novel method based on the deep neural network and support vector machine approaches is investigated to build up a thickness classification model by incorporating different input features, including the dielectric constants of the material under test, which are extracted from the scattering parameters proceeded by the National Institute of Standards and Technology iterative method. The attained classification results from both machine learning algorithms are then compared and show that both of the models have a good prediction ability. While the deep neural network is the better solution with a large amount of data, the support vector machine is the more appropriate solution when employing small dataset. It can be stated that the proposed method is able to support systematic monitoring as it can help to improve the accuracy of the prediction of material thickness.