Advances of deep learning applications in ground-penetrating radar: A survey

Zheng, Tong; Gao, Jie; Yuan, Dongdong

doi:10.1016/j.conbuildmat.2020.120371

Cited by 102 publications

(23 citation statements)

References 113 publications

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“…During the last decade, most of the efforts in GPR developments within this application field include new processing techniques and methodologies for 3D reconstruction and visualization, machine learning techniques for automatic detection (mainly objects with a circular section such as bars and pipes, but also cracks), and data digitization into GIS models [60][61][62][63][64][65][66][67]. Moreover, new multi-antenna systems and array multi-channel antennas have been developed, thus allowing for a dense 3D data collection [68], thus reducing the surveying time and increasing the productivity.…”

Section: Gpr Applications In Civil Engineeringmentioning

confidence: 99%

From Its Core to the Niche: Insights from GPR Applications

2022

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Thanks to its non-destructive, high-resolution imaging possibilities and its sensitivity to both conductive and dielectric subsurface structures, Ground-Penetrating Radar (GPR) has become a widely recognized near-surface geophysical tool, routinely adopted in a wide variety of disciplines. Since its first development almost 100 years ago, the domain in which the methodology has been successfully deployed has significantly expanded from ice sounding and environmental studies to precision agriculture and infrastructure monitoring. While such expansion has been clearly supported by the evolution of technology and electronics, the operating principles have always secured GPR a predominant position among alternative inspection approaches. The aim of this contribution is to provide a large-scale survey of the current areas where GPR has emerged as a valuable prospection methodology, highlighting the reasons for such prominence and, at the same time, to suggest where and how it could be enhanced even more.

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Section: Gpr Applications In Civil Engineeringmentioning

confidence: 99%

From Its Core to the Niche: Insights from GPR Applications

2022

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“…Larger databases are compiled and used in the study of pavement distress using deep learning methods, showing that the frequency of the antenna also affects the results because of the detail loss [252]. In [253] a review of deep learning applications in GPR is presented, comparing the results and grouping the works depending on the type of data used. The conclusion is that the methods using A-scans [254,255] present slightly better results than those using B-scans [252,256] or C-scans [257,258].…”

Section: Recent Developments and Applicationsmentioning

confidence: 99%

“…Notwithstanding, this processing requires more complex architecture and a large volume of datasets. The optimal solution to achieve a proper development in the deep learning based on C-scans is to create a big GPR dataset, sharing data from research around the world [253].…”

Section: Recent Developments and Applicationsmentioning

confidence: 99%

A Review of GPR Application on Transport Infrastructures: Troubleshooting and Best Practices

2021

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The non-destructive testing and diagnosis of transport infrastructures is essential because of the need to protect these facilities for mobility, and for economic and social development. The effective and timely assessment of structural health conditions becomes crucial in order to assure the safety of the transportation system and time saver protocols, as well as to reduce excessive repair and maintenance costs. Ground penetrating radar (GPR) is one of the most recommended non-destructive methods for routine subsurface inspections. This paper focuses on the on-site use of GPR applied to transport infrastructures, namely pavements, railways, retaining walls, bridges and tunnels. The methodologies, advantages and disadvantages, along with up-to-date research results on GPR in infrastructure inspection are presented herein. Hence, through the review of the published literature, the potential of using GPR is demonstrated, while the main limitations of the method are discussed and some practical recommendations are made.

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“…Inversion and migration approaches (e.g., [12,13]) were developed based on Maxwell's equations to eliminate signal interference, but excessive derivative and integral calculations are time-consuming. Machine learning (e.g., [14,15]) provides automatic approaches for target identification from radargrams, but required considerable datasets with similar underground attributes are difficult to acquire from field measurement. Jin and Duan [16] developed a discrete grid method to eliminate hyperbolic interference in rock detection, but with many parameters determined by experience.…”

Section: Introductionmentioning

confidence: 99%

Identification of Unstable Subsurface Rock Structure Using Ground Penetrating Radar: An EEMD-Based Processing Method

Yang

Duan

2020

Applied Sciences

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Surrounding rock quality of underground caverns is crucial to structural safety and stability in geological engineering. Classic measures for rock quality investigation are destructive and time consuming, and therefore technology evolution for efficiently evaluating rock quality is significantly required. In this paper, the non-destructive technology ground penetrating radar (GPR) assisted by an ensemble empirical mode decomposition (EEMD)-based signal processing approach is investigated for identifying unstable subsurface rock structures. By decomposing the pre-processed GPR signals into multiple intrinsic mode functions (IMFs) and residues, one typical IMF can preserve the distinct local modes and is considered to reconstruct the subterranean profile. Promising results have been achieved in simple scenarios and filed measurements. The reconstructed profiles can accurately illustrate the subsurface interfaces and eliminate the interference signals. Unstable rock structures have been identified in further field applications. Therefore, the developed approach is efficient in unstable rock structure identification.

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Advances of deep learning applications in ground-penetrating radar: A survey

Cited by 102 publications

References 113 publications

From Its Core to the Niche: Insights from GPR Applications

From Its Core to the Niche: Insights from GPR Applications

A Review of GPR Application on Transport Infrastructures: Troubleshooting and Best Practices

Identification of Unstable Subsurface Rock Structure Using Ground Penetrating Radar: An EEMD-Based Processing Method

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