Robotic Inspection of Underground Utilities for Construction Survey Using a Ground Penetrating Radar

Feng, Jinglun; Yang, Liang; Hoxha, Ejup; Jiang, Biao; Xiao, Jizhong

doi:10.1061/(asce)cp.1943-5487.0001062

Cited by 7 publications

(2 citation statements)

References 47 publications

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“…Numerical simulations, both for the sensitivity tests and the forward modelling, were performed exploiting gprMax , version 3.1.6 (Warren et al., 2016), which is an open‐source software designed to simulate the propagation of an EM wave even in heterogenous media, by solving Maxwell's equations in 3‐D using the finite‐difference time‐domain method. The algorithm can handle complex geometries and materials distributions, being highly adaptable to model a wide range of subsurface scenarios in various fields of application, such as archaeology, civil engineering, glaciology, and hydrogeology, among others (e.g., Cheng et al., 2023; Feng et al., 2023; Haruzi et al., 2022; Hillebrand et al., 2021; Pajewski et al., 2017; Schennen et al., 2022). In order to reduce the computational costs due to model discretization, we exploited a specific module for gprMax modelling on GPU (Warren et al., 2018) and performed the inversion on Cineca Marconi 100 cluster with 2 CPUs with 16 cores 3.1 GHz, 4 NVIDIA Volta V100 16GB GPUs and 256 GB RAM per node running on GPUs and parallelized on several nodes.…”

Section: Methodsmentioning

confidence: 99%

GPR modelling and inversion to quantify the debris content within ice

Santin,

Roncoroni,

Forte

et al. 2023

Near Surface Geophysics

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Scattering is often detected when ground‐penetrating radar (GPR) surveys are performed on glaciers at different latitudes and in various environments. This event is often seen as an undesirable feature on data, but it can be exploited to quantify the debris content in mountain glaciers through a dedicated scattering inversion approach. At first, we considered the possible variables affecting the scattering mechanisms, namely the dielectric properties of the scatterers, their size, shape and quantity, as well as the wavelength of the electromagnetic (EM) incident field to define the initial conditions for the inversion. Each parameter was independently evaluated with forward modelling tests to quantify its effect in the scattering mechanism. After extensive tests, we found that the dimension and the amount of scatterers are the crucial parameters. We further performed modelling randomizing the scatterer distribution and dimension, critically evaluating the stability of the approach and the complexity of the models. After the tests on synthetic data, the inversion procedure was applied to field datasets, acquired on the Eastern Gran Zebrù glacier (Central Italian Alps). The results show that even a low percentage of debris can produce high scattering. The proposed methodology is quite robust and able to provide quantitative estimates of the debris content within mountain glaciers in different conditions.

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Section: Methodsmentioning

confidence: 99%

GPR modelling and inversion to quantify the debris content within ice

Santin,

Roncoroni,

Forte

et al. 2023

Near Surface Geophysics

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“…Yet another approach employing deep neural network architectures 44 has been used to acquire permittivity inversion of geometrical configurations of buried objects. In addition to this methodology, subsurface pipes have been detected and localized on GPR image with deep learning based back projection algorithm 45 and subsurface targets with different shapes could be reconstructed with deep learning networks 46 , 47 .…”

Section: Introductionmentioning

confidence: 99%

Variable data structures and customized deep learning surrogates for computationally efficient and reliable characterization of buried objects

Yurt,

Torpi,

Kizilay

et al. 2024

Sci Rep

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In this study, in order to characterize the buried object via deep-learning-based surrogate modeling approach, 3-D full-wave electromagnetic simulations of a GPR model have been used. The task is to independently predict characteristic parameters of a buried object of diverse radii allocated at different positions (depth and lateral position) in various dispersive subsurface media. This study has analyzed variable data structures (raw B-scans, extracted features, consecutive A-scans) with respect to computational cost and accuracy of surrogates. The usage of raw B-scan data and the applications for processing steps on B-scan profiles in the context of object characterization incur high computational cost so it can be a challenging issue. The proposed surrogate model referred to as the deep regression network (DRN) is utilized for time frequency spectrogram (TFS) of consecutive A-scans. DRN is developed with the main aim being computationally efficient (about 13 times acceleration) compared to conventional network models using B-scan images (2D data). DRN with TFS is favorably benchmarked to the state-of-the-art regression techniques. The experimental results obtained for the proposed model and second-best model, CNN-1D show mean absolute and relative error rates of 3.6 mm, 11.8 mm and 4.7%, 11.6% respectively. For the sake of supplementary verification under realistic scenarios, it is also applied for scenarios involving noisy data. Furthermore, the proposed surrogate modeling approach is validated using measurement data, which is indicative of suitability of the approach to handle physical measurements as data sources.

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