GPR Applications Across Engineering and Geosciences Disciplines in Italy: A Review

Benedetto, Andrea; Tosti, Fabio; Ciampoli, Luca Bianchini; D’Amico, Fabrizio

doi:10.1109/jstars.2016.2554106

Cited by 33 publications

(21 citation statements)

References 103 publications

(99 reference statements)

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“…It is well known that GPR has been extensively used for non-destructive testing problems [17], [18], [19] with mainstream civil engineering applications that include: mapping of utilities [20], [21], bridge evaluation [22], [23], road inspection [24], [25], [26], pipe location, [27], [28] and crack detection [29], [30]. The mapping of reinforcement in concrete using GPR [31] is particularly attractive due to the high contrast in dielectric properties between concrete and steel, combined with the fact that concrete is a relatively homogeneous medium which further increases the overall signal-to-clutter ratio.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning-Based Fast-Forward Solver for Ground Penetrating Radar With Application to Full-Waveform Inversion

Giannakis

Giannopoulos

Warren

2019

IEEE Trans. Geosci. Remote Sensing

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The simulation, or forward modeling, of Ground Penetrating Radar (GPR) is becoming a more frequently used approach to facilitate interpretation of complex real GPR data, and as an essential component of full-waveform inversion (FWI). However, general full-wave 3D electromagnetic (EM) solvers, such as ones based on the Finite-Difference Time-Domain (FDTD) method, are still computationally demanding for simulating realistic GPR problems. We have developed a novel near realtime, forward modeling approach for GPR that is based on a machine learning (ML) architecture. The ML framework uses an innovative training method which combines a predictive principal component analysis technique, a detailed model of the GPR transducer, and a large dataset of modeled GPR responses from our FDTD simulation software. The ML-based forward solver is parameterized for a specific GPR application, but the framework can be applied to many different classes of GPR problems. To demonstrate the novelty and computational efficiency of our ML-based GPR forward solver, we used it to carry out FWI for a common infrastructure assessment application-determining the location and diameter of reinforcement bars in concrete. We tested our FWI with synthetic and real data, and found a good level of accuracy in determining the rebar location, size, and surrounding material properties from both datasets. The combination of the near real-time computation, which is orders of magnitude less than what is achievable by traditional full-wave 3D EM solvers, and the accuracy of our ML-based forward model is a significant step towards commercially-viable applications of FWI of GPR.

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Section: Introductionmentioning

confidence: 99%

A Machine Learning-Based Fast-Forward Solver for Ground Penetrating Radar With Application to Full-Waveform Inversion

Giannakis

Giannopoulos

Warren

2019

IEEE Trans. Geosci. Remote Sensing

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“…Ground Penetrating Radar (GPR) is well known as a viable equipment for locating buried archaeological remains [7][8][9][10][11]. Its effectiveness is mostly due to a wide range of available antenna frequency systems, which implies different possible depths and resolution of investigation, as well as to the enormous amount of information retrieved and possibility to obtain a tomographic plan view of the area investigated.…”

Section: B Geophysical Prospections In Bath Complexesmentioning

confidence: 99%

Structural detailing of buried Roman baths through GPR inspection

Ciampoli

Santarelli

Loreti

et al. 2020

Archaeological Prospection

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This paper deals with a geophysical experimental activity carried out in the Maxentius Complex, an archaeological site located in Rome, Italy. The objective of this study was to evaluate the feasibility of GPR for the structural detailing of buried roman baths structures. As a result, GPR allowed to confirm the literature-based information, i.e. to precisely locate the tanks of the thermal area. Their presents was already known through previous excavation then buried and no more visible. In addition, the tomographic analysis highlighted the presence of two further tanks, thereby suggesting the possibility of further rooms to be located close to the known ones. This assumption was also confirmed by tomographic analysis, which stressed out a wall pattern that seems to suggest the presence of further rooms in the top-right side of the area. In general terms, GPR demonstrated a great applicability to archaeological purposes, despite the reliability and productivity of the data interpretation are strongly influenced by the expertise of both the geophysicists and the archaeologists involved.

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“…*valerio.gagliardi@uniroma3.it; phone +39 06 57333617; Currently, several on-site non-destructive testing (NDT) technologies and sensors are available for subsidence monitoring and displacement mapping. Amongst others, accelerometers [2], strain gauges [3], Global Position System (GPS), levelling [4][5], Ground Penetrating Radar (GPR), Infrared Thermography (IRT) [6][7][8][9][10] and terrestrial SAR Interferometry [11], are recognised as viable technologies for infrastructure monitoring.…”

Section: Introductionmentioning

confidence: 99%

Monitoring of bridges by MT-InSAR and unsupervised machine learning clustering techniques

Gagliardi¹,

Tosti

Ciampoli

et al. 2021

Earth Resources and Environmental Remote Sensing/Gis Applications XII

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Continuous monitoring of critical infrastructures is crucial to prevent catastrophic events such as collapse of viaducts and prioritising maintenance interventions. However, developing effective monitoring approaches must rely on the collection of numerous information, such as the time series of structural deformations. In this context, various groundbased non-destructive testing (NDT) methods have been used in monitoring the structural integrity of transport infrastructures. These require routine and systematic application at the network level over long periods of time to build up a solid database of information, involving many efforts from stakeholders and asset owners in the sector. To this effect, satellite-based remote sensing techniques, such as the Multi-temporal Interferometric Synthetic Aperture Radar (MT-InSAR), have gained momentum due to the provision of accurate cumulative structural displacements in bridges. Although the application of the InSAR monitoring technique is established, this is limited by the high amount of time required for the interpretation of data with high spatial and temporal density. This research aims to demonstrate the viability of MT-InSAR techniques for the structural assessment of bridges and the monitoring of damage by structural subsidence, using high-resolution SAR datasets, integrated with complementary Ground-Based (GB) information. To this purpose, high-resolution SAR dataset of the COSMO-SkyMed (CSK) mission provided by the Italian Space Agency (ASI), were acquired and processed in the framework of the ASI-Open Call approved Project "MoTiB" (ID 742). To elaborate, a Persistent Scatterer Interferometry (PSI) analysis is applied to identify and monitor the structural displacements at the Rochester Bridge, in Rochester, Kent, UK. To explore the viability of Machine Learning algorithms in detecting critical scenarios in the monitoring phases, an Unsupervised ML Clustering approach, which generates homogeneous and well-separated clusters, is implemented. Each PS data-point is allocated to specific cluster groups, based on individual deformation trend features and the values of displacements from the historical time-series. This research paves the way for the development of a novel interpretation approach relying on the integration between remote-sensing technologies and on-site surveys to improve upon current maintenance strategies for bridges and transport assets.

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GPR Applications Across Engineering and Geosciences Disciplines in Italy: A Review

Cited by 33 publications

References 103 publications

A Machine Learning-Based Fast-Forward Solver for Ground Penetrating Radar With Application to Full-Waveform Inversion

A Machine Learning-Based Fast-Forward Solver for Ground Penetrating Radar With Application to Full-Waveform Inversion

Structural detailing of buried Roman baths through GPR inspection

Monitoring of bridges by MT-InSAR and unsupervised machine learning clustering techniques

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