VIS-NIR, SWIR and LWIR Imagery for Estimation of Ground Bearing Capacity

Ground bearing capacity has become a relevant concept for site-specific management that aims to protect soil from the compaction and the rutting produced by the indiscriminate use of agricultural and forestry machines. Nevertheless, commonly known techniques for its estimation are cumbersome and time-consuming. In order to alleviate these difficulties, this paper introduces an innovative sensory system based on Visible-Near InfraRed (VIS-NIR), Short-Wave InfraRed (SWIR) and Long-Wave InfraRed (LWIR) imagery and a sequential algorithm that combines a registration procedure, a multi-class SVM classifier, a K-means clustering and a linear regression for estimating the ground bearing capacity. To evaluate the feasibility and capabilities of the presented approach, several experimental tests were carried out in a sandy-loam terrain. The proposed solution offers notable benefits such as its non-invasiveness to the soil, its spatial coverage without the need for exhaustive manual measurements and its real time operation. Therefore, it can be very useful in decision making processes that tend to reduce ground damage during agricultural and forestry operations.The authors acknowledge funding from the European commission in the 7th Framework Programme (CROPS Grant Agreement No. 246252) and partial funding under ROBOCITY2030-III-CM project (Robótica aplicada a la mejora de la calidad de vida de los ciudadanos. Fase III; S2013/MIT-2748), funded by Programa de Actividades I + D en la Comunidad de Madrid and cofunded by Structural Funds of the EU. Héctor Montes also acknowledges support from Universidad Tecnológica de Panamá.We acknowledge support of the publication fee by the CSIC Open Access Publication Support Initiative through its Unit of Information Resources for Research (URICI).CHF 1,620 APC fee funded by the EC FP7 Post-Grant Open Access PilotPeer reviewe

Radar Technology

In this book “Radar Technology”, the chapters are divided into four main topic areas: Topic area 1: “Radar Systems” consists of chapters which treat whole radar systems, environment and target functional chain. Topic area 2: “Radar Applications” shows various applications of radar systems, including meteorological radars, ground penetrating radars and glaciology. Topic area 3: “Radar Functional Chain and Signal Processing” describes several aspects of the radar signal processing. From parameter extraction, target detection over tracking and classification technologies. Topic area 4: “Radar Subsystems and Components” consists of design technology of radar subsystem components like antenna design or waveform design

Multi-Channel Ground-Penetrating Radar for the Continuous Quantification of Snow and Firn Density, Depth, and Accumulation

A priority of ice sheet surface mass balance (SMB) prediction is ascertaining the surface density and annual snow accumulation. These forcing data are inputs for firn density models and can be used to inform remotely sensed ice sheet surface processes and to assess Regional Climate Model (RCM) skill. The Greenland Traverse for Accumulation and Climate Studies (GreenTrACS) retrieved 16 shallow firn cores and dug 42 snow pits along the Western percolation zone of the Greenland Ice Sheet (GrIS) during May and June of 2016 and 2017. I deployed and maintained a multi-channel 500 MHz ground-penetrating radar in a multi-offset configuration throughout the two traverse campaigns. The multi-channel radar technique accurately and independently estimates density, depth, and annual snow accumulation -- between the firn core and snow pit sites -- by horizon velocity analysis of common midpoint radar reflections from the snow and shallow firn. I analyzed a 45 km section of the traverse in a high accumulation zone, known as the GreenTrACS Core 15 Western Spur. Deviations in surface density up to +- 15 kg/m3 from the transect mean correlate with surface elevation and surface slope angle. Spatial variation in mean annual accumulation of ~0.175 m w.e. ɑ-1 occurs across a trough in the surface topography ~5 km wide. The reported variability of density and accumulation demonstrates that RCMs must be down-scaled to resolutions within 5 km to assess subtle yet significant contributions to the GrIS SMB

Experimental and theoretical investigation on road pavements and materials through ground-penetrating radar

Ground-penetrating radar (GPR) is being increasingly used over the last years in a wide range of applications, due to its flexibility and high potential to provide characterization and imaging of structures and materials. Overall, several reasons are contributing to increase the demand for the use of this tool and non-destructive testing techniques (NDTs) in general. Amongst all, it is worth citing technological advances of both hardware and software elements, an intrinsic lower significance of measurements provided by traditional monitoring techniques along with their greater invasiveness in measuring processes and, last but not least, the impacts of Global Economic Crisis on the use of economic resources affecting for years countries worldwide. The combination of such factors has led the interest of several skill profiles spanning from researchers, practitioners and end-users in general, and focused the attention of governments and local authorities on the high capabilities to gather a large amount of information in a relatively short time of surveying. In the field of pavement engineering, GPR has been used since the early 1980s mostly focusing on the geometrical characterization of road structure, by evaluating layer thicknesses. Minor care has been given to the analysis of the main causes of damage and performance properties of pavements, in order to improve management of infrastructural asset through effective and efficient maintenance and rehabilitation actions, as well as to provide best conditions in design of new roads. In that regard, this thesis is aimed to give a useful contribution also in the perspective of road safety issues by improving current processes of management and maintenance of road asset, along with the design of new roads, and provide effective support for the application and practical use of the tools described. Efforts have been spent in order to detect and quantify those physical and strength characteristics of road materials and subgrade soils that are relevant causes of damage, such that an effective planning of supporting actions for maintenance, rehabilitation and design of new roads may be timely performed. Three main topics are addressed, namely: i) the evaluation of moisture spatial field in subgrade soils through a self-consistent frequency-based technique and the analysis of radar support scale in small-scale measurements of water content; ii) the potential to detect and quantify clay content in load-bearing layers and subgrade soils through different GPR tools and signal processing techniques, and iii) the possibility to infer strength and deformation characteristics of both bound, unbound pavement structures, and subgrade soils from their electric properties. The results are encouraging for applications in the field of pavement engineering

Development of GPR data analysis algorithms for predicting thin asphalt concrete overlay thickness and density

Thin asphalt concrete (AC) overlay is a commonly used asphalt pavement maintenance strategy. The thickness and density of thin AC overlay are important to achieving proper pavement performance, which can be evaluated using ground-penetrating radar (GPR). The traditional methods for predicting pavement thickness and density relies on the accurate determination of electromagnetic (EM) signal reflection amplitude and time delay. Due to the limitation of GPR antenna bandwidth, the range resolution of the GPR signal is insufficient for thin pavement layer evaluation. To this end, the objective of this study is to develop signal processing techniques to increase the resolution of GPR signals, such that they can be applied to thin AC overlay evaluation. First, the generic GPR forward 2-D imaging scheme is discussed. Then two linear inversion techniques are proposed, including migration and sparse reconstruction. Both algorithms were validated on GPR signals reflected from buried pipes using finite difference time domain (FDTD) simulation. Second, as a special case of the 2-D GPR imaging and linear inversion reconstruction, regularized deconvolution was applied to GPR signals reflected from thin AC overlays. Four types of regularization methods, including Tikhonov regularization and total variation regularization, were compared in terms of accuracy in estimating thin pavement layer thickness. The L-curve method was used to identify the appropriate regularization parameter. A subspace method—a multiple signal classification (MUSIC) algorithm—was then utilized to increase the resolution of 3-D GPR signals. An extended common midpoint (XCMP) method was used to find the dielectric constant and the thickness of the thin AC overlay at a full-scale test section. The results show that the MUSIC algorithm is an effective approach for increasing the 3-D GPR signal range resolution when the XCMP method is applied on thin AC overlay. Furthermore, a non-linear inversion technique is proposed based on gradient descent. The proposed non-linear optimization algorithm was applied on real GPR data reflected from thin AC overlay and the thickness and density prediction results are accurate. Finally, a “modified reference scan” approach was developed to eliminate the effect of AC pavement surface moisture on GPR signals, such that the density of thin AC overlay can be monitored in real time during compaction

A GPR-GPS-GIS-integrated, information-rich and error-aware system for detecting, locating and characterizing underground utilities

Underground utilities have proliferated throughout the years. The location and dimension of many underground utilities have not always been properly collected and documented, leading to utility conflicts and utility strikes, and thus resulting in property damages, project delays, cost overruns, environment pollutions, injuries and deaths. The underlying reasons are twofold. First, the reliable data regarding the location and dimension of underground utility are missing or incomplete. Existing methods to collect data are not efficient and effective. Second, positional uncertainties are inherent in the measured utility locations. An effective means is not yet available to visualize and communicate the inherent positional uncertainties associated with utility location data to end-users (e.g., excavator operator). To address the aforementioned problems, this research integrate ground penetrating radar (GPR), global positioning system (GPS) and geographic information system (GIS) to form a total 3G system to collect, inventory and visualize underground utility data. Furthermore, a 3D probabilistic error band is created to model and visualize the inherent positional uncertainties in utility data. ^ Three main challenges are addressed in this research. The first challenge is the interpretation of GPR and GPS raw data. A novel method is created in this research to simultaneously estimate the radius and buried depth of underground utilities using GPR scans and auxiliary GPS data. The proposed method was validated using GPR field scans obtained under various settings. It was found that this newly created method increases the accuracy of estimating the buried depth and radius of the buried utility under a general scanning condition. The second challenge is the geo-registration of detected utility locations. This challenge is addressed by integration of GPR, GPS and GIS. The newly created system takes advantages of GPR and GPS to detect and locate underground utilities in 3D and uses GIS for storing, updating, modeling, and visualizing collected utility data in a real world coordinate system. The third challenge is positional error/uncertainty assessment and modeling. The locational errors of GPR system are evaluated in different depth and soil conditions. Quantitative linkages between error magnitudes and its influencing factors (i.e., buried depths and soil conditions) are established. In order to handle the positional error of underground utilities, a prototype of 3D probabilistic error band is created and implemented in GIS environment. This makes the system error-aware and also paves the way to a more intelligent error-aware GIS. ^ To sum up, the newly created system is able to detect, locate and characterize underground utilities in an information-rich and error-aware manner

Scour detection with monitoring methods and machine learning algorithms - a critical review

Foundation scour is a widespread reason for the collapse of bridges worldwide. However, assessing bridges is a complex task, which requires a comprehensive understanding of the phenomenon. This literature review first presents recent scour detection techniques and approaches. Direct and indirect monitoring and machine learning algorithm-based studies are investigated in detail in the following sections. The approaches, models, characteristics of data, and other input properties are outlined. The outcomes are given with their advantages and limitations. Finally, assessments are provided at the synthesis of the research.This research was funded by FCT (Portuguese national funding agency for science, research, and technology)/MCTES through national funds (PIDDAC) under the R&D Unit Institute for Sustainability and Innovation in Structural Engineering (ISISE), under reference UIDB/04029/2020 and trough the doctoral Grant 2021.06162.BD. This work has also been partly financed within the European Horizon 2020 Joint Technology Initiative Shift2Rail through contract no. 101012456 (IN2TRACK3)

A Study of clutter reduction techniques in wide bandwidth HF/VHF deep ground penetrating radar

Reducing clutter is one of the most daunting problems a radar processing engineer faces. Clutter causes a significant problem when attempting to detect sub-surface targets, as any significant change in the ground dielectric will produce a return at the receiver. The difficulty in reducing the clutter is compounded by the fact that the spectral characteristics of the clutter are similar to that of the target. While there are many methods that exist to reduce clutter, few do not require a priori information of either the target or the clutter. There are applications, of interest to the electromagnetic community, that are restricted in the amount of a priori information available to them. Estimation-subtraction filters calculate an estimate of the clutter from the statistics of the data collected and subtract that estimate from the original data. The Wiener filter has long been used as a way to suppress noise signals when a target reference is known. Using it to reduce clutter is a relatively new area of research. This research proposes estimation-subtraction filters and an application of the Wiener filter, which do not require a priori information to reduce the clutter of a bi-static synthetic aperture based, wideband deep ground penetrating radar system. The results of applying these filters to data collected in this way, at these depths, are illustrated here for the first time