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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: closed (31 January 2022) | Viewed by 32968

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Special Issue Editors

Prof. Dr. Tarek Zayed

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Guest Editor

Department of Building and Real Estate (BRE), Faculty of Construction and Environment (FCE), The Hong Kong Polytechnic University, ZN716 Block Z Phase 8 Hung Hom, Kowloon, Hong Kong 999077, China
Interests: construction engineering and management; asset management; simulation in construction; sustainability assessment and implementation; resilience of infrastructure
Special Issues, Collections and Topics in MDPI journals

Dr. Thikra Dawood

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Guest Editor

Polytechnic Institute, Purdue University, West Lafayette, IN, USA
Interests: Infrastructure management and asset performance; simulation modeling; artificial intelligence and machine learning; smart and sustainable systems; multi-sensory data fusion; data analytics; risk Assessment

Dr. Mona Abouhamad

E-Mail Website
Guest Editor

Faculty of Engineering, Cairo University, Cairo, Egypt
Interests: asset management; condition assessment; lean construction, life cycle cost analysis; productivity analysis; life cycle assessment, infrastructure assessment; life cycle analysis; risk management

Dr. Mohammed Alsharqawi

E-Mail Website
Guest Editor

Township of South Stormont, Long Sault, ON K0C 1P0, Canada
Interests: infrastructure asset management; performance-based maintenance contracting; sustainable and resilient infrastructure systems; data analytics for engineering management sciences

Special Issue Information

Dear Colleagues,

The relatively new foray into condition assessment via Ground Penetrating Radar (GPR) synchronized with the aging crisis of civil infrastructure worldwide. GPR is an expedient, high-resolution, and remote sensing technology for nondestructive evaluation of several infrastructure systems. Thus, it has become the tool of choice in the constructive visualization and condition assessment of several types of infrastructure, such as buildings, highways, bridge decks, subways, and foundation systems, as well as in detecting and locating underground objects and utilities. In the past decades, numerous research works have benefited from the advancements and innovations of GPR technology to develop different assessment models and algorithms for civil infrastructure systems.

This Special Issue focuses on the practical application of GPR to address real-world problems across a wide range of civil infrastructure issues. It aims at collecting new developments and methodologies, best practices, and applications of GPR to civil infrastructure systems. We welcome submissions that provide the community with the most recent advancements on all aspects of GPR, including but not limited to the following:

Applications of GPR in inspecting concrete structures;
GPR as a non-destructive inspection technique;
GPR-based condition assessment and deterioration models of concrete structures;
GPR-based rehabilitation planning of concrete structures;
Performance of GPR technology in defect detection;
Innovative GPR signal and data processing techniques;
Novel GPR data interpretation methods;
GPR-based corrosion mapping;
Integration of GPR and other sensing technologies;
Applications of GPR in inspecting other types of civil infrastructure systems;
GPR-based decision support systems for asset management.

Prof. Dr. Tarek Zayed
Dr. Thikra Dawood
Dr. Mona Abouhamad
Dr. Mohammed Alsharqawi
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

Ground Penetrating Radar (GPR)
Non-Destructive Evaluation (NDE)
Inspection
Condition Assessment
Deterioration Assessment and Monitoring
Corrosiveness Mapping
Concrete Structures
Infrastructure and Asset Management
Decision Making.

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Published Papers (11 papers)

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Editorial

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2 pages, 162 KiB

Open AccessEditorial

Special Issue “Ground Penetrating Radar (GPR) Applications in Civil Infrastructure Systems”

by Tarek Zayed, Thikra Dawood, Mona Abouhamad and Mohammed Alsharqawi

Remote Sens. 2022, 14(22), 5682; https://doi.org/10.3390/rs14225682 - 10 Nov 2022

Cited by 3 | Viewed by 1286

Abstract

This Special Issue includes a collection of papers that address the practical applications of GPR to various civil infrastructure systems [...] Full article

(This article belongs to the Special Issue Ground Penetrating Radar (GPR) Applications in Civil Infrastructure Systems)

Research

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23 pages, 16656 KiB

Open AccessArticle

Integrated Archaeological Modeling Based on Geomatics Techniques and Ground-Penetrating Radar

by Rami Al-Ruzouq, Saleh Abu Dabous, Abdelrahman Abueladas, Fatma Hosny and Fakhariya Ibrahim

Remote Sens. 2022, 14(7), 1622; https://doi.org/10.3390/rs14071622 - 28 Mar 2022

Cited by 7 | Viewed by 2912

Abstract

Archaeological sites have been subjected to preservation efforts due to their significant historical and cultural value, as well as their vulnerability. Several advanced digital imageries and modeling technologies have been proposed in the literature and adopted in practice to obtain accurate data required to manage and restore archaeological sites. This study proposes an integrated data collection and analysis methodology combining aerial and close-range photogrammetry, geographic information systems (GIS), and ground-penetrating radar (GPR) technologies to capture essential geospatial and geophysical information for preserving archaeological sites. The integrated methodology was applied and demonstrated with data and information collected from the important archaeological site of Qaser Amra, which is an ancient castle located in the desert in Jordan. The proposed methodology generated various levels of details, including a 2.5-dimensional geo-reference model, a GIS vector site layout, and a three-dimensional (3D) textured model. Subsurface detection of anomalies was performed across the site using the GPR technology. Most anomalies indicated shallow subsurface walls buried within depths ranging from half to one meter and at different extensions. Moreover, based on the GPR data, the horizontal and vertical extent of Qaser Amra’s walls were defined using 3D imaging. The integrated 3D archaeological model of Qaser Amra can be used for archaeological documentation, maintenance and monitoring, conservation, tourism, and urban planning. Full article

(This article belongs to the Special Issue Ground Penetrating Radar (GPR) Applications in Civil Infrastructure Systems)

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20 pages, 5119 KiB

Open AccessArticle

Deterioration Mapping of RC Bridge Elements Based on Automated Analysis of GPR Images

by Mohammed Abdul Rahman, Tarek Zayed and Ashutosh Bagchi

Remote Sens. 2022, 14(5), 1131; https://doi.org/10.3390/rs14051131 - 25 Feb 2022

Cited by 5 | Viewed by 2495

Abstract

Ground-Penetrating Radar (GPR) is a popular non-destructive technique for evaluating RC bridge elements as it can identify major subsurface defects within a short span of time. The data interpretation of the GPR profiles based on existing amplitude-based approaches is not completely reliable when compared to the actual condition of concrete with destructive measures. An alternative image-based analysis considers GPR as an imaging tool wherein an experienced analyst marks attenuated areas and generates deterioration maps with greater accuracy. However, this approach is prone to human errors and is highly subjective. The proposed model aims to improve it through automated detection of hyperbolas in GPR profiles and classification based on mathematical modeling. Firstly, GPR profiles are pre-processed, and hyperbolic reflections were detected in them based on a trained classifier using the Viola–Jones Algorithm. The false positives are eliminated, and missing regions are identified automatically across the top/bottom layer of reinforcement based on user-interactive regional comparison and statistical analysis. Subsequently, entropy, a textural factor, is evaluated to differentiate the detected regions closely equivalent to the human visual system. These detected regions are finally clustered based on entropy values using the K-means algorithm and a deterioration map is generated which is robust, reliable, and corresponds to the in situ state of concrete. A case study of a parking lot demonstrated good correspondence of deterioration maps generated by the developed model when compared with both amplitude- and image-based analysis. These maps can facilitate structural inspectors to locally identify deteriorated zones within structural elements that require immediate attention for repair and rehabilitation. Full article

(This article belongs to the Special Issue Ground Penetrating Radar (GPR) Applications in Civil Infrastructure Systems)

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Graphical abstract

13 pages, 7797 KiB

Open AccessCommunication

Gabor Filter-Based Segmentation of Railroad Radargrams for Improved Rail Track Condition Assessment: Preliminary Studies and Future Perspectives

by Gerald Zauner, David Groessbacher, Martin Buerger, Florian Auer and Giuseppe Staccone

Remote Sens. 2021, 13(21), 4293; https://doi.org/10.3390/rs13214293 - 26 Oct 2021

Cited by 2 | Viewed by 2220

Abstract

Ground penetrating radar (GPR) has been used for several years as a non-contact and non-destructive measurement method for rail track analysis with the aim of recording the condition of ballast and substructures. As the recorded data sets typically cover a distance of many kilometers, the evaluation of these data involves considerable effort and costs. For this reason, there is an increasing need for automated support in the evaluation of GPR measurement data. This paper presents an image segmentation pipeline based on 2D Gabor filter texture analysis, which can assist users in GPR data-based track condition assessment. Gabor filtering is used to transform a radargram image (or B-scan) into a high-dimensional, multi-resolution representation. Principal component analysis (PCA) is then applied to reduce the data content to three characteristic dimensions (namely amplitude, frequency, and local scattering) to finally obtain a segmented radargram image representing different classes of relevant image structures. From these results, quantitative measures can be derived that allow experts an improved condition assessment of the rail track. Full article

(This article belongs to the Special Issue Ground Penetrating Radar (GPR) Applications in Civil Infrastructure Systems)

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22 pages, 14483 KiB

Open AccessArticle

A Comprehensive Evaluation for the Tunnel Conditions with Ground Penetrating Radar Measurements

by Jordi Mahardika Puntu, Ping-Yu Chang, Ding-Jiun Lin, Haiyina Hasbia Amania and Yonatan Garkebo Doyoro

Remote Sens. 2021, 13(21), 4250; https://doi.org/10.3390/rs13214250 - 22 Oct 2021

Cited by 13 | Viewed by 3220

Abstract

We aim to develop a comprehensive tunnel lining detection method and clustering technique for semi-automatic rebar identification in order to investigate the ten tunnels along the South-link Line Railway of Taiwan (SLRT). We used the Ground Penetrating Radar (GPR) instrument with a 1000 MHz antenna frequency, which was placed on a versatile antenna holder that is flexible to the tunnel’s condition. We called it a Vehicle-mounted Ground Penetrating Radar (VMGPR) system. We detected the tunnel lining boundary according to the Fresnel Reflection Coefficient (FRC) in both A-scan and B-scan data, then estimated the thinning lining of the tunnels. By applying the Hilbert Transform (HT), we extracted the envelope to see the overview of the energy distribution in our data. Once we obtained the filtered radargram, we used it to estimate the Two-dimensional Forward Modeling (TDFM) simulation parameters. Specifically, we produced the TDFM model with different random noise (0–30%) for the rebar model. The rebar model and the field data were identified with the Hierarchical Agglomerative Clustering (HAC) in machine learning and evaluated using the Silhouette Index (SI). Taken together, these results suggest three boundaries of the tunnel lining i.e., the air–second lining boundary, the second–first lining boundary, and the first–wall rock boundary. Among the tunnels that we scanned, the Fangye 1 tunnel is the only one in category B, with the highest percentage of the thinning lining, i.e., 13.39%, whereas the other tunnels are in category A, with a percentage of the thinning lining of 0–1.71%. Based on the clustered radargram, the TDFM model for rebar identification is consistent with the field data, where k = 2 is the best choice to represent our data set. It is interesting to observe in the clustered radargram that the TDFM model can mimic the field data. The most striking result is that the TDFM model with 30% random noise seems to describe our data well, where the rebar response is rough due to the high noise level on the radargram. Full article

(This article belongs to the Special Issue Ground Penetrating Radar (GPR) Applications in Civil Infrastructure Systems)

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16 pages, 448 KiB

Open AccessArticle

PUMA Applied to Time Delay Estimation for Processing GPR Data over Debonded Pavement Structures

by Bachir Tchana Tankeu, Vincent Baltazart, Yide Wang and David Guilbert

Remote Sens. 2021, 13(17), 3456; https://doi.org/10.3390/rs13173456 - 31 Aug 2021

Cited by 3 | Viewed by 3167

Abstract

In this paper, principal-singular-vector utilization for modal analysis (PUMA) was adapted to perform time delay estimation on ground-penetrating radar (GPR) data by taking into account the shape of the transmitted GPR signal. The super-resolution capability of PUMA was used to separate overlapping backscattered echoes from a layered pavement structure with some embedded debondings. The well-known root-MUSIC algorithm was selected as a benchmark for performance assessment. The simulation results showed that the proposed PUMA performs very well, especially in the case where the sources are totally coherent, and it requires much less computational time than the root-MUSIC algorithm. Full article

(This article belongs to the Special Issue Ground Penetrating Radar (GPR) Applications in Civil Infrastructure Systems)

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14 pages, 8468 KiB

Open AccessArticle

GPR Virtual Guidance System for Subsurface 3D Imaging

by Gabbo P. H. Ching, Ray K. W. Chang, Tess X. H. Luo and Wallace W. L. Lai

Remote Sens. 2021, 13(11), 2154; https://doi.org/10.3390/rs13112154 - 31 May 2021

Cited by 9 | Viewed by 2879

Abstract

Three-dimensional GPR imaging requires evenly and densely distributed measurements, ideally collected without the need for ground surface markings, which is difficult to achieve in large-scale surveys. In this study, a guidance system was developed to guide the GPR operator to walk along a predesigned traverse, analogous to the flight path design of an airborne drone. The guidance system integrates an auto-track total station unit (ATTS), and by estimating the real-time offset angle and distance, guidance corrections can be provided to the operator in real time. There are two advantages: (1) reduced survey time as grid marking on the ground is no longer needed and (2) accurate positioning of each traverse. Lab and field experiments were conducted in order to validate the guidance system. The results show that with the guidance system, the survey paths were better defined and followed in terms of feature connectivity and resolution of images, and the C-scans generated were closer to the real subsurface world. Full article

(This article belongs to the Special Issue Ground Penetrating Radar (GPR) Applications in Civil Infrastructure Systems)

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23 pages, 15251 KiB

Open AccessArticle

A Study of the Application and the Limitations of GPR Investigation on Underground Survey of the Korean Expressways

by Ji-Young Rhee, Keon-Tae Park, Jin-Woo Cho and Sang-Yum Lee

Remote Sens. 2021, 13(9), 1805; https://doi.org/10.3390/rs13091805 - 6 May 2021

Cited by 16 | Viewed by 4418

Abstract

In this study, the applications and the limitations of the Ground-penetrating radar (GPR) investigation have been addressed with the main objective of improving the efficient GPR application of subsurface surveys on Korean expressways. The depth of investigation and detection performance of anomalous objects have been studied using two different types of multichannel GPR on the Korean Expressway Corporation’s nondestructive testing testbed for subsurface detection. Based on the field survey, it was found that utilizing the plane view by depth, cross-sectional and longitudinal profile data of the multichannel GPR simultaneously, analysis and evaluation of the GPR signals are more efficient and practical. Although there was a difference in the frequency of use, the precision difference between two GPR is almost similar in the investigation depth and detection performance of the pavement subsurface anomaly. Under an asphalt concrete standard pavement section, the effective depth of cavity detection is 1–1.5 m, while detection under concrete pavement is less than 1.0 m. In addition, there is still a need to calibrate depths using field cores when constructing a 3D underground facility map. Full article

(This article belongs to the Special Issue Ground Penetrating Radar (GPR) Applications in Civil Infrastructure Systems)

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Other

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24 pages, 32972 KiB

Open AccessTechnical Note

Subsoil Recognition for Road Investment Supported by the Integration of Geodetic and GPR Data in the Form of a Point Cloud

by Łukasz Ortyl and Marta Gabryś

Remote Sens. 2021, 13(19), 3886; https://doi.org/10.3390/rs13193886 - 28 Sep 2021

Cited by 3 | Viewed by 2161

Abstract

During road construction investments, the key issue affecting the structure’s safety is accurate subsoil recognition. Identifying subsoil variability zones or natural voids can be performed using geophysical methods, and ground-penetrating radar (GPR) is recommended for this task as it identifies the location and spatial range karst formations. This paper describes the methodology of acquisition and processing of GPR data for ground recognition for road investment. Additional subsoil research was performed after karst phenomena were identified in the investment area, formations not revealed by geological recognition from earlier studies during the pre-design stage. Mala Ramac CU II radar with a 250 MHz antenna and a Leica DS2000 with 250 and 700 MHz antennas with real-time geopositioning were used to obtain the data. Regarding GPR data postprocessing, we present a method of converting spatial visualization into a point cloud that allows for GPR and geodetic data integration and confrontation. This approach enabled us to determine the locations of control trenches, the results of which were used for material validation, which is necessary to improve the reliability of subsoil recognition. The results showed a high correlation between the recorded GPR signals and the subsoil structure. Additionally, differences in the quality of results for measurements conducted before laying supporting layers with slag and on the completed road structure surface are illustrated. Full article

(This article belongs to the Special Issue Ground Penetrating Radar (GPR) Applications in Civil Infrastructure Systems)

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19 pages, 12262 KiB

Open AccessTechnical Note

2D Wavelet Decomposition and F-K Migration for Identifying Fractured Rock Areas Using Ground Penetrating Radar

by Yang Jin and Yunling Duan

Remote Sens. 2021, 13(12), 2280; https://doi.org/10.3390/rs13122280 - 10 Jun 2021

Cited by 10 | Viewed by 2990

Abstract

The quality of the surrounding rock is crucial to the stability of underground caverns, thereby requiring an effective monitoring technology. Ground-penetrating radar (GPR) can reconstruct the subterranean profile by electromagnetic waves, but two significant issues, called clutter and hyperbola tails, affect the signal quality. We propose an approach to identify fractured rocks using 2D Wavelet transform (WT) and F-K migration. F-K migration can handle the hyperbola using Fourier analysis. WT can mitigate clutter, distinguish signal discontinuity, and provide signals with a good time-frequency resolution for F-K migration. In the simulation, the migration result from horizontal detail coefficients highlight the crack locations and reduce the scattering signals. Noise has been separated by 2D WT. Hyperbola tails are decomposed to vertical and diagonal detail coefficients. Similar promising results have been achieved in the field measurement. Therefore, the proposed approach can process GPR signals for identifying fractured rock areas. Full article

(This article belongs to the Special Issue Ground Penetrating Radar (GPR) Applications in Civil Infrastructure Systems)

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15 pages, 6253 KiB

Open AccessTechnical Note

Analysis of Forward Model, Data Type, and Prior Information in Probabilistic Inversion of Crosshole GPR Data

by Hui Qin, Zhengzheng Wang, Yu Tang and Tiesuo Geng

Remote Sens. 2021, 13(2), 215; https://doi.org/10.3390/rs13020215 - 10 Jan 2021

Cited by 9 | Viewed by 2321

Abstract

The crosshole ground penetrating radar (GPR) is a widely used tool to map subsurface properties, and inversion methods are used to derive electrical parameters from crosshole GPR data. In this paper, a probabilistic inversion algorithm that uses Markov chain Monte Carlo (MCMC) simulations within the Bayesian framework is implemented to infer the posterior distribution of the relative permittivity of the subsurface medium. Close attention is paid to the critical elements of this method, including the forward model, data type and prior information, and their influence on the inversion results are investigated. First, a uniform prior distribution is used to reflect the lack of prior knowledge of model parameters, and inversions are performed using the straight-ray model with first-arrival traveltime data, the finite-difference time-domain (FDTD) model with first-arrival traveltime data, and the FDTD model with waveform data, respectively. The cases using first-arrival traveltime data require an unreasonable number of model evaluations to converge, yet are not able to recover the real relative permittivity field. In contrast, the inversion using the FDTD model with waveform data successfully infers the correct model parameters. Then, the smooth constraint of model parameters is employed as the prior distribution. The inversion results demonstrate that the prior information barely affects the inversion results using the FDTD model with waveform data, but significantly improves the inversion results using first-arrival traveltime data by decreasing the computing time and reducing uncertainties of the posterior distribution of model parameters. Full article

(This article belongs to the Special Issue Ground Penetrating Radar (GPR) Applications in Civil Infrastructure Systems)

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