Ground Penetrating Radar Algorithm to Sense the Depth of Blood Clot in Microwave Head Imaging

Background: Microwave imaging is one of the emerging non-invasive portable imaging techniques, which uses nonionized radiations to take a detailed view of biological tissues in the microwave frequency range. Brain stroke is an emergency caused by the interruption of the blood supply into parts of brain, leading to the loss of millions of brain cells. Imaging plays a major role in stroke diagnosis for prompt treatment. Objective: This work proposes a computationally efficient algorithm called the GPR algorithm to locate the blood clot with a size of 10 mm in microwave images. Methods: The electromagnetic waves are radiated, and backscattered reflections are received by Antipodal Vivaldi antenna with the parasitic patch (48 mm*21 mm). The received signals are converted to a planar 2D image, and the depth of the blood clot is identified from the B-scan image. The novelty of this work lies in applying the GPR algorithm for the accurate positioning of a blood clot in a multilayered head tissue. Results: The proposed system is effectively demonstrated using a 3D EM simulator and simulated results are verified in a Vector network analyzer (E8363B) with an experimental setup. Conclusion: This an alternative safe imaging modality compared to present imaging systems(CT and MRI)

Urban water supply infrastructure in Grudziądz (northern Poland): from the Middle Ages to the pre-modern times

This article presents the history of the water supply system in Grudziądz (Poland) over the centuries, from the Middle Ages to the end of the nineteenth century. The location of Grudziądz on the high escarpment of the Vistula River made it difficult to supply water to the town. The innovative technical facilities, such as a water-work and water supply tower had to be constructed because the gravitational waterworks could not be applied. The basis for the research was the analysis of historical sources. In this study, non-invasive methods were used (aerial prospection, LiDAR scanning and geophysical surveys with Ground Penetrating Radar), since the hydrotechnical objects are located in a functioning urban space and no excavations could be carried out. The research included: (i) the measurements, exploration and preparation of photo-documentation of the water tower, (ii) providing a digital model of the tower, (iii) finding the tunnel inside the tower, and (iv) attempting to locate the tunnel's course outside the tower.

Integrating Non-Destructive Surveys into a Preliminary BIM-Oriented Digital Model for Possible Future Application in Road Pavements Management

The implementation of the digitalization of the linear infrastructure is growing rapidly and new methods for developing BIM-oriented digital models are increasing. The integration of the results obtained from non-destructive surveys carried out along a road infrastructure in a pavement digital model can be a useful method for developing an efficient process from a pavement management systems (PMS) point of view. In fact, several applications to optimize PMS have been thoroughly investigated over the years and several researchers and scientists have investigated significant elements for improving the PMS applied to a transport network, including road infrastructures. This study presents a new, tentative process for implementing into a BIM environment the dataset processed from two surveys carried out in a case study. Moreover, the main reason for this investigation is related to the need for an effective system able to evaluate continuously the pavement conditions and programming maintenance interventions. To date, both the instruments and the methods to detect the pavement configuration have evolved, along with the development of non-destructive technology (NDT) tools such as laser-scanners and ground-penetrating radar. Finally, the main results of the research demonstrate the possibility to provide a digital twin model from the synergistic use of geometric and design information with the results from monitoring conducted on a road infrastructure. The model can be potentially used in future BIM-based PMS applications.

Long-term mass-balance monitoring and evolution of ice in caves through structure from motion–multi-view stereo and ground-penetrating radar techniques

This study investigates the application of a terrestrial structure from motionmulti-view stereo (SfM-MVS) approach combined with ground-penetrating radar (GPR) surveys for monitoring the surface topographic change of two permanent ice deposits in caves located in the Julian Alps (south-eastern European Alps). This method allows accurate calculation of both seasonal and annual mass balance, estimating the amount of ice inside caves. The ground-based SfM approach represents a low-cost workflow with very limited logistical problems of transportation and human resources and a fast acquisition time, all key factors in such extreme environments. Under optimal conditions, SfM-MVS allows sub-centimetric resolution results, comparable to more expensive and logistically demanding surveys such as terrestrial laser scanning (TLS). Fourteen SfM acquisitions were made between the 2017–2020 ablation seasons (i.e. July–October) while 2 GPR surveys were acquired in 2012. The obtained dense point clouds and digital terrain models (DTMs) made possible a reliable calculation of topographic changes and mass balance rates during the analysed period. The integration of SfM-MVS products with GPR surveys provided comprehensive imaging of the ice thickness and the total ice volume present in each of the caves, proving to be a reliable, low cost and multipurpose methodology ideal for long-term monitoring.

RCE-GAN: A Rebar Clutter Elimination Network to Improve Tunnel Lining Void Detection from GPR Images

Ground penetrating radar (GPR) is one of the most recommended tools for routine inspection of tunnel linings. However, the rebars in the reinforced concrete produce a strong shielding effect on the electromagnetic waves, which may hinder the interpretation of GPR data. In this work, we proposed a method to improve the identification of tunnel lining voids by designing a generative adversarial network-based rebar clutter elimination network (RCE-GAN). The designed network has two sets of generators and discriminators, and by introducing the cycle-consistency loss, the network is capable of learning high-level features between unpaired GPR images. In addition, an attention module and a dilation center part were designed in the network to improve the network performance. Validation of the proposed method was conducted on both synthetic and real-world GPR images, collected from the implementation of finite-difference time-domain (FDTD) simulations and a controlled physical model experiment, respectively. The results demonstrate that the proposed method is promising for its lower demand on the training dataset and the improvement in the identification of tunnel lining voids.

Investigation structural settlement by Ground Penetrating Radar (Case study)

Electromagnetic wave is transferred by the GPR (ground penetrating radar), and A geotechnical application may benefit from this non-destructive test. This study is proposed to estimate the type and soil problem location that causes differential settlement of a structure (pumping station) by GPR surveying. The survey is achieved before and after the treatment by cement injection method to identify the locations that took cement injections as a full injection, partial or not at all using two types of antennas (160,450) MHz. The study also will estimate the thickness of the foundation by GPR and comparing it with actually executed. The results showed the creeping soil has occurred in some parts of the soil under the foundation, and after soil treatment, most of these parts were taken injection, and others did not. Also, it was found the relatively high accuracy of GPR for detecting the thickness of the raft foundation.

Mapping Ground Penetrating Radar Amplitudes Using Artificial Neural Network and Multiple Regression Analysis Methods

Bridges are aging and deteriorating. Thus, the development of Bridge Management Systems (BMSs) became imperative nowadays. Condition assessment is one of the most critical and vital components of BMSs. Ground Penetrating Radar (GPR) is one of the non-destructive techniques (NDTs) that are used to evaluate the condition of bridge decks which are subjected to the rebar corrosion. The objective of the proposed method is to develop standardized amplitude scale for bridge decks based on a hybrid optimization-decision making model. Shuffled frog leaping algorithm is employed to compute the optimum thresholds. Then, polynomial regression and artificial neural network models are designed to predict the prioritizing index based on a set of multi-criteria decision-making methods. The weibull distribution is utilized to capture the stochastic nature of deterioration of concrete bridge decks. Lastly, a case study is presented to demonstrate the capabilities of the proposed method.