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Ground Penetrating Radar is a multidisciplinary Nondestructive Evaluation technique that requires knowledge of electromagnetic wave propagation, material properties and antenna theory. Under some circumstances this tool may require auxiliary algorithms to improve the interpretation of the collected data. Detection, location and definition of target’s geometrical and physical properties with a low false alarm rate are the objectives of these signal post-processing methods. Basic approaches are focused in the first two objectives while more robust and complex techniques deal with all objectives at once. This work reviews the use of Artificial Neural Networks and Machine Learning for data interpretation of Ground Penetrating Radar surveys. We show that these computational techniques have progressed GPR forward from locating and testing to imaging and diagnosis approaches.

Although ground penetrating radar (GPR) technology is commonly used to assess the condition of reinforced-concrete (RC) bridge decks, the GPR data interpretation is not straightforward. Further, the thresholds that define the severity of deterioration are selected arbitrarily. This paper aims to solve a problem associated with GPR results generated by using a numerical amplitude method to assess corrosiveness of bridge decks.

Data, for more than 50 different bridge decks, were collected using a ground-coupled antenna. Depth-correction was performed for the collected data to normalize the reflected amplitude. Using k-means clustering technique, the amplitude values of each bridge deck were classified into four categories. Later, statistical analysis was performed where the threshold values of different categories of corrosion and deterioration are chosen. Monte-Carlo simulation technique was used to validate the value of these thresholds. Moreover, a sensitivity analysis was performed to realize the effect of changing the thresholds in the areas of corrosion.

The final result of this research is a four-category (good, fair, poor and critical) GPR scale with three fixed numerical thresholds (−7.71 dB, −10.04 dB and −14.63 dB) that define these categories. Besides, deterioration curves have been modeled using Weibull function and based on GPR outputs and corrosion areas.

The developed numerical GPR-based scale and deterioration models are expected to help the decision-makers in assessing the corrosiveness of bridge decks accurately and objectively. Hence, they will be able to take the right intervention decision for managing these decks.

Utility strikes have spawned companies specializing in providing a priori analyses of the underground. Geophysical techniques such as Ground Penetrating Radar (GPR) are harnessed for this purpose. However, analyzing GPR data is labour-intensive and repetitive. It may therefore be worthwhile to amplify this process by means of Machine Learning (ML). In this work, harnessing the ADR design science methodology, an Intelligence Amplification (IA) system is designed that uses ML for decision-making with respect to utility material type. It is driven by three novel classes of Convolutional Neural Networks (CNNs) trained for this purpose, which yield accuracies of 81.5% with outliers of 86%. The tool is grounded in the available literature on IA, ML and GPR and is embedded into a generic analysis process. Early validation activities confirm its business value.

In this paper, a high-frequency radar test system was used to collect the data of clean ballast bed and fouled ballast bed of ballasted tracks, respectively, for a quantitative evaluation of the condition of railway ballast bed.

Based on original radar signals, the time–frequency characteristics of radar signals were analyzed, five ballast bed condition characteristic indexes were proposed, including the frequency domain integral area, scanning area, number of intersections with the time axis, number of time-domain inflection points and amplitude envelope obtained by Hilbert transform, and the effectiveness and sensitivity of the indexes were analyzed.

The thickness of ballast bed tested at the sleep bottom by high-frequency radar is up to 55 cm, which meets the requirements of ballast bed detection. Compared with clean ballast bed, the values of the five indexes of fouled ballast bed are larger, and the five indexes could effectively show the condition of the ballast bed. The computational efficiency of amplitude envelope obtained by Hilbert transform is 140 s·km⁻¹, and the computational efficiency of other indexes is 5 s·km⁻¹. The amplitude envelopes obtained by Hilbert transform in the subgrade sections and tunnel sections are the most sensitive, followed by scanning area. The number of intersections with the time axis in the bridge sections was the most sensitive, followed by the scanning area. The scanning area can adapt to different substructures such as subgrade, bridges and tunnels, with high comprehensive sensitivity.

The research can provide appropriate characteristic indexes from the high-frequency radar original signal to quantitatively evaluate ballast bed condition under different substructures.

The paper aims to provide a review of how innovations in laser, acoustics, radar, magnetic and other sensor technologies are aiding in making unmanned vehicles more autonomous.

In‐depth interviews are carried out with exhibitors of sensors at the AUVSI exhibition.

Innovations in infrared, laser, acoustics, magnetic and other sensor technologies are helping unmanned vehicles better meet the challenge of an ever‐increasing range of applications in military, law enforcement, and commercial applications as well as agriculture, fishing and rescue operations.

These sensor innovations will help make robot applications of all types more autonomous, easier to create and more cost effective in unmanned as well as manufacturing, logistics, medical and other applications.

The paper provides an insight into some of the latest in laser, radar, acoustic, magnetic, accelerometer, vision and gyro sensors and how they are helping address robotic applications that one might have seen if they had been on the exhibition floor at the Las Vegas unmanned vehicle show (AUVSI) in 2012.

The purpose of this paper is to investigate the numerical aspects of the electromagnetic scattering of a plane wave by a set of buried cylinders.

The cylindrical wave approach is employed. The analytical model is implemented in a Fortran code. The numerical aspects of the technique are presented, with particular emphasis on the numerical evaluation of the integrals involved in the procedure.

The tool obtained allows a fast computation of the electromagnetic field scattered by an arbitrary disposition of circular cylinders below an interface. Comparisons with the finite element method are proposed, showing the very good agreement between the results obtained with the two different approaches.

The advantages of the proposed technique in terms of computational weight are explained. The method can be useful in a wide class of application, e.g. in the ground penetrating radar applications, microscopy, biomedical applications, etc.

To introduce a new, low‐cost and easy‐to‐use leak detection system to help water utilities improve their effectiveness in locating leaks. The paper also presents an overview of leakage management strategies including acoustic and other leak detection techniques.

The design approach was based on the use personal computers as a platform and enhanced signal processing algorithms. This eliminated the need for a major component of the usual hardware of leak pinpointing correlators which reduced the system's cost; made it easy to use, and improved the effectiveness of locating leaks in all types of pipes.

Effectiveness of the new leak detection system for pinpointing leaks was demonstrated using real world examples. The system has promising potential for all water utilities, including small and medium‐sized ones and utilities in developing countries.

The leak detection system presented in the paper will help all water utilities, including small and medium‐sized ones and utilities in developing countries, to save water by dramatically improving their effectiveness in locating leaks in all types of pipes.

The paper presents information about a new effective system for locating leaks in water distribution pipes. Effective leak detection tools are needed by water utilities worldwide.