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Sensing, Processing and Data Fusion for Non-destructive Testing and Earth Observation

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

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 10243

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Special Issue Editors

Prof. Dr. Andreas Loizos

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Laboratory of Pavement Engineering, National Technical University of Athens (NTUA), Athens, Greece
Interests: ground-penetrating radar; deflection-based assessment methods; fiber-optic sensors; pavement and material engineering; roadway and airfield pavement evaluation; non-destructive testing; civil engineering
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Prof. Dr. Amir M. Alani

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School of Computing and Engineering, University of West London, St Mary’s Rd, Ealing, London W5 5RF, UK
Interests: ground-penetrating radar; signal processing; modelling and simulation; remote sensing; non-destructive testing; concrete technology; forestry engineering; soil engineering; civil engineering
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Prof. Dr. Francesco Benedetto

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Department of Engineering, Roma Tre University, Via Vito Volterra 62, 00146 Rome, Italy
Interests: ground-penetrating radar; radar signal processing; software and cognitive radio; digital signal and image processing for telecommunications and economics
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Dr. Valerio Gagliardi

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Guest Editor

Department of Civil, Computer Science and Aeronautical Technologies Engineering, Roma Tre University, Via Vito Volterra 62, 00146 Rome, Italy
Interests: infrastructure monitoring; road pavements; persistent scatterers; SAR interferometry; sensors; bridge monitoring; structural monitoring; remote sensing; surface displacements; urban subsidence analysis; critical infrastructure monitoring road maintenance; bridge management systems
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Dr. Francesco Soldovieri

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National Research Council of Italy (CNR), Institute for Electromagnetic Sensing of the Environment (IREA), Via Diocleziano 328, 80124 Naples, Italy
Interests: electromagnetic scattering; radar imaging; ground penetrating radar; data integration; non-invasive monitoring tools
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Prof. Dr. Fabio Tosti

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1. School of Computing and Engineering, University of West London, Room BY.03.19, St. Mary’s Rd., Ealing, London W5 5RF, UK
2. The Faringdon Centre for Non-Destructive Testing and Remote Sensing, University of West London, Room BY.GF.015, St. Mary’s Rd., Ealing, London W5 5RF, UK
Interests: ground-penetrating radar; signal processing; remote sensing; deflection-based methods; numerical simulations; forestry engineering; airfield and highway pavement engineering; construction materials; civil engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Non-destructive testing (NDT) and Earth observation (EO) methods are crucial for monitoring and assessing conditions and changes in the natural and anthropogenic environment. Related techniques rely on sensing technologies of different physical nature and working principles, capable of returning information at multiple scales and resolutions, although they are substantially different in terms of the time frequency of data acquisition and spatial coverage capabilities. On the one hand, the technological advancement and progress achieved in data processing and interpretation have allowed tremendous hardware and software developments; on the other, the development of new data fusion and technology integration paradigms is providing further opportunities to bridge information gaps and take research in the field to the next level.

This Special Issue aims to provide a comprehensive overview of state-of-the-art applications, numerical and theoretical developments of sensing techniques, data processing and interpretation methods, data fusion, integration and correlation within the context of NDT and EO.

Areas of interest include, but are not limited to, the following set of topics:

Sensor types, systems and operating modes (active/passive sensors; remote and ground-based, embedded sensing systems; stand-alone and integrated multi-source sensing modes);
Advanced processing methods and information analysis techniques (analogue/digital signal processing; multi-dimensional signal processing; image processing; optical/acoustic/vibration/electromagnetic signal processing; data processing and information analysis; inversion approaches);
Multi-sensor, multi-temporal and multi-modal data fusion and integration (image fusion; spatio-temporal data fusion; multi-source data matching and co-registration; artificial intelligence and machine learning for data fusion and integration);
The integration of NDT and EO data into smart platforms and information systems and architectures (GIS, Cloud-based Information Systems; DSS);
The development of fully deployed and prototype NDT and EO hardware and software;
New NDT applications and EO missions and downstream implementations;
The contribution of NDT and EO to the development of new standards, policies and best practices;
Case studies relevant to environmental and anthropogenic diagnostics and monitoring.

Prof. Dr. Andreas Loizos
Prof. Dr. Amir M. Alani
Prof. Dr. Francesco Benedetto
Dr. Valerio Gagliardi
Dr. Francesco Soldovieri
Prof. Dr. Fabio Tosti
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

sensing systems
non-destructive testing
Earth observation
remote sensing
advanced processing methods, information analysis techniques and inversion approaches
multi-sensor, multi-temporal and multi-modal data fusion and integration
NDT and EO data for smart information systems
NDT and EO hardware and software development
new NDT applications and EO missions and downstream implementations
new standards, policies and best practices for NDT and EO methods

Published Papers (6 papers)

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Research

18 pages, 6429 KiB

Open AccessArticle

Condition Rating of Bridge Decks with Fuzzy Sets Modeling for SF-GPR Surveys

by Nicolas Gagarin, Dimitrios Goulias and James Mekemson

Remote Sens. 2023, 15(14), 3631; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15143631 - 21 Jul 2023

Cited by 2 | Viewed by 685

Abstract

Highway agencies monitor the condition of thousands of bridge decks every year. Even though Ground Penetrating Radar (GPR) has been used in bridge-deck evaluation, Step-Frequency GPR (SF-GPR) provides advanced condition assessment yet requires extensive and complex post-processing analysis. An SF-GPR analysis system was recently developed and used for monitoring the condition of all the bridge decks in the state of Maryland. The objective of this study was to develop a bridge deck condition rating approach using fuzzy sets modeling on the SF-GPR data and analysis. The fuzzy sets membership functions needed to reflect rating score categories similar to those considered in the National Bridge Inventory (NBI) database for uniformity. Thus, the fuzzy sets modeling was built considering nine condition membership functions. The overall bridge deck condition score leading to each of the nine condition states was based on both physical and condition-related bridge deck parameters as obtained from the SF-GPR analysis. The modeling approach is presented herein, along with two bridge deck examples. The proposed novel fuzzy sets modeling can be considered for possible adoption elsewhere where similar GPR systems are used. Full article

(This article belongs to the Special Issue Sensing, Processing and Data Fusion for Non-destructive Testing and Earth Observation)

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21 pages, 7098 KiB

Open AccessArticle

by Ruiqing Shen, Yonghui Zhao, Hui Cheng, Shufan Hu, Shifeng Chen and Shuangcheng Ge

Remote Sens. 2023, 15(13), 3250; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15133250 - 24 Jun 2023

Viewed by 976

Abstract

Ground-penetrating radar (GPR) is a well-respected, effective, and efficient geophysical technique. However, for underwater engineering detection and underwater archaeology, the measured B-scan profiles typically contain surface-related multiple waves, which can reduce the signal to noise ratio and interfere with the interpretation of results. SRME is a feedback iteration method based on wave equation, which is frequently utilized in marine seismic explorations but very rarely in GPR underwater engineering detection. To fill this gap, we applied SRME to suppress multiples that appear in GPR underwater images. When we compared the effectiveness of the underwater horizontal layered model and the underwater undulating interface model, we found a high match rate between the predicted and the real-world multiples. In addition, the addition of the Gaussian random noise level with a 4% maximum amplitude to the B-scan profile of the horizontal stratified model yielded satisfactory multiple suppression results. Finally, we applied this method to the B-scan GPR section of actual underwater archaeological images to achieve multiple suppression, which can more effectively weaken and inhibit the surface-related multiples. Both numerical simulations and actual field data show that the SRME method is highly suitable for interpreting waterborne GPR data, and more accurate interpretation can be obtained from the GPR profile after multiples suppression. Full article

(This article belongs to the Special Issue Sensing, Processing and Data Fusion for Non-destructive Testing and Earth Observation)

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16 pages, 4017 KiB

Open AccessArticle

Using NDT Data to Assess the Effect of Pavement Thickness Variability on Ride Quality

by Christina Plati, Konstantina Georgouli and Andreas Loizos

Remote Sens. 2023, 15(12), 3011; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15123011 - 08 Jun 2023

Cited by 1 | Viewed by 1012

Abstract

Pavement condition largely determines its long-term behavior and is of paramount importance for rehabilitation and maintenance management. The use of non-destructive testing (NDT) systems to assess pavement condition has gained much popularity. Often, well-known NDT systems are combined to take full advantage of the capabilities of each system. Combining independent NDT systems to optimize the assessment process is a scientific challenge. With this in mind, the purpose of this paper is to investigate the extent to which data from two independent NDT systems can be combined: pavement thickness obtained with ground penetrating radar (GPR) and roughness data obtained with a road surface profiler (RSP). In particular, the objective of this study is to determine whether the expected variations in asphalt layer thickness, due to the construction process and the different pavement cross sections along the same road/highway road, may have an impact on pavement roughness as expressed in International Roughness Index (IRI) values. GPR and roughness data are collected, processed, and analyzed. The analysis results show that thickness variations are reflected in pavement roughness. The greater the variation in asphalt layer thickness, the greater the IRI values. Furthermore, it is argued that the GPR capabilities can be used for an initial assessment of the expected pavement quality. Full article

(This article belongs to the Special Issue Sensing, Processing and Data Fusion for Non-destructive Testing and Earth Observation)

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24 pages, 6100 KiB

Open AccessArticle

Algorithm Fusion for 3D Ground-Penetrating Radar Imaging with Field Examples

by Yih Jeng, Hung-Ming Yu and Chih-Sung Chen

Remote Sens. 2023, 15(11), 2886; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15112886 - 01 Jun 2023

Viewed by 1526

Abstract

Numerous data processing algorithms are available for ground-penetrating radar (GPR) data processing. However, most of the existing processing algorithms are derived from Fourier theory and assume that the system is linear or that data are stationary, which may oversimplify the case. Some nonlinear algorithms are accessible for improvement but generally are for stationary and deterministic systems. To alleviate the dilemma, this study proposes an algorithm fusion scheme that employs standard linear techniques in conjunction with a newer nonlinear and non-stationary method. The linear techniques include linear filtering, migration, and interpolation. The newer method is mainly for nonlinear filtering and image reconstruction. The results can be demonstrated in a two-dimensional single profile (time–distance section) or a 3D visualization if survey lines fulfill the 3D Nyquist sample intervals requirement. Two controlled experiments were conducted to justify the proposed scheme. Then, a field study including two examples was carried out to demonstrate the feasibility of practical applications. Compared with conventional methods, the proposed algorithm fusion provides better visualization and integrative interpretation for GPR imaging. Full article

(This article belongs to the Special Issue Sensing, Processing and Data Fusion for Non-destructive Testing and Earth Observation)

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18 pages, 6981 KiB

Open AccessArticle

Combined GPR and Self-Potential Techniques for Monitoring Steel Rebar Corrosion in Reinforced Concrete Structures: A Laboratory Study

by Giacomo Fornasari, Luigi Capozzoli and Enzo Rizzo

Remote Sens. 2023, 15(8), 2206; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15082206 - 21 Apr 2023

Cited by 1 | Viewed by 2224

Abstract

Steel rebar corrosion is one of the main causes of the deterioration of engineering reinforced structures. Steel rebar in concrete is normally in a non-corroding, passive condition, but these conditions are not always achieved in practice, due to which corrosion of rebars takes place. This degradation has physical consequences, such as decreased ultimate strength and serviceability of engineering concrete structures. This work describes a laboratory test where GPR and SP geophysical techniques were used to detect and monitor the corrosion phenomena. The laboratory tests have been performed with several reinforced concrete samples. The concrete samples were partially submerged in water with a 5% sodium chloride (NaCl) solution. Therefore, an accelerated corrosion phenomenon has been produced by a direct current (DC) power supply along the rebar. The geophysical measurements were performed with a 2.0 GHz centre frequency GPR antenna along several parallel lines on the samples, always being the radar line perpendicular to the rebar axis. The GPR A-scan amplitude signals were elaborated with the Hilbert Transform approach, observing the envelope variations due to the progress of the steel rebar corrosion in each concrete sample. Moreover, Self-Potential acquisitions were carried out on the surface of the concrete sample at the beginning and end of the experiments. Each technique provided specific information, but a data integration method used in the operating system will further improve the overall quality of diagnosis. The collected data were used for an integrated detection approach useful to observe the corrosion evolution along the reinforcement bar. These first laboratory results highlight how the GPR should give a quantitative contribution to the deterioration of reinforced concrete structure. Full article

(This article belongs to the Special Issue Sensing, Processing and Data Fusion for Non-destructive Testing and Earth Observation)

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21 pages, 21589 KiB

Open AccessArticle

Frequency Domain Electromagnetic System Based on Unmanned Aerial Vehicles Platform for Detecting Shallow Subsurface Targets

by Shiyan Li, Kang Xing and Xiaojuan Zhang

Remote Sens. 2023, 15(3), 754; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15030754 - 28 Jan 2023

Cited by 1 | Viewed by 1586

Abstract

Due to the advantages of being nondestructive, rapid, and convenient, the electromagnetic detection method has attracted growing interest in the field of shallow subsurface detection. With the rapid development of unmanned aerial vehicle (UAV) technology, the use of the UAV platform for measurement can not only improve work efficiency but also avoid the significant losses that may be caused by humans working in dangerous areas. Therefore, we propose a broadband frequency domain electromagnetic system AFEM-3 based on a UAV platform for shallow subsurface targets detection (within less than 2 m). The sensor head adopts a concentric planar coil structure with a high spatial resolution, and a bucking coil connected in reverse series with the transmitting coil is used to suppress the primary field at the receiving coil. We designed a transmitting module based on unipolar frequency multiplication sinusoidal pulse width modulation technology that can generate multi-frequency arbitrary combination transmitting waveforms with low total harmonic distortion. It can also be matched to a variety of different transmitter coils by using the same hardware circuit. In addition, the global navigation satellite system and inertial measurement unit are integrated on the sensor head. The measurement response value, position, and attitude information can be displayed in real-time through the host computer. Through the static experiment of a standard coil, we verified the consistency between the AFEM-3 system with the theory. The performance of the system was evaluated through field experiments. The experimental results show that the system can effectively detect multiple metal targets in shallow subsurface areas. For different metal targets, the AFEM-3 system can provide obvious frequency domain characteristics. Full article

(This article belongs to the Special Issue Sensing, Processing and Data Fusion for Non-destructive Testing and Earth Observation)

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