GPR application on construction foundation study

Amran, Tengku Sarah Tengku; Ismail, Mohamad Pauzi; Ismail, Mohamed A.; Amin, Mohamad Syafiq Mohd; Ahmad, Megat Harun Al Rashid Megat; Basri, N S M

doi:10.1088/1757-899x/271/1/012089

Cited by 4 publications

(2 citation statements)

References 3 publications

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“…At present, this method is widely used in road disease detection, underground pipelines, tunnel cavity detection, and many others [1][2][3]. Underground target detection is one of the typical cases in the application of ground-penetrating radar, which can effectively detect voids and steel bars in underground structures [4,5]. However, at present, this technology is not mature enough.…”

Section: Introductionmentioning

confidence: 99%

GPR Target Recognition Based on Improved YOLOv3-SPP

Zhong¹,

Li²,

Mo³

et al. 2023

Preprint

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When ground-penetrating radar is used to detect targets within concrete, the location of the targets, the identification of different shapes, properties and less obvious echoes all greatly increase the interpretation time of the staff and can easily cause misjudgment of the echo images. In this paper, the ground-penetrating radar echo images (B-scan) after processing are mean filtered to eliminate the direct waves that interfere greatly with the echoes. The RFB-s structure is added to the YOLOv3-SPP network structure, while the Anchor value is optimized and the EIOU loss function is introduced. For four types of data with different shapes and properties at random target locations, three models, YOLOv3, YOLOv3-SPP and the improved YOLOv3-SPP, are used for classification and identification, and the proposed algorithm models are comprehensively evaluated using model evaluation metrics. The experimental results show that the algorithm models proposed in this paper have good recognition effect in ground-penetrating radar echo image target detection.

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Section: Introductionmentioning

confidence: 99%

GPR Target Recognition Based on Improved YOLOv3-SPP

Zhong¹,

Li²,

Mo³

et al. 2023

Preprint

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“…With this information, changes in physical properties (geologic interfaces and other features), electromagnetic wave velocities, and depths to interfaces and features can be estimated. GPR is a widely utilized technique that has applications in geological investigations (e.g., Campbell et al 2017), archaeology (e.g., Ciampoli et al 2020;Ristić et al 2020), construction and engineering (e.g., Morcous and Erdogmus 2009;Amran et al 2017), environmental remediation (e.g., Steelman et al 2017;Shao et al 2018), military operations (Abeynayake and Tran 2016), and other industries. A major limitation of using GPR, however, is that a subject matter expert (SME) must post-process and interpret the data, which makes widespread use less likely.…”

Section: Introductionmentioning

confidence: 99%

Automated ground-penetrating-radar post-processing software in R programming

Cook¹,

Torres²,

Lamie³

et al. 2022

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Ground-penetrating radar (GPR) is a nondestructive geophysical technique used to create images of the subsurface. A major limitation of GPR is that a subject matter expert (SME) needs to post-process and interpret the data, limiting the technique’s use. Post-processing is time-intensive and, for detailed processing, requires proprietary software. The goal of this study is to develop automated GPR post-processing software, compatible with Geophysical Survey Systems, Inc. (GSSI) data, in open-source R programming. This would eliminate the need for an SME to process GPR data, remove proprietary software dependencies, and render GPR more accessible. This study collected GPR profiles by using a GSSI SIR4000 control unit, a 100 MHz antenna, and a Trimble GPS. A standardized method for post-processing data was then established, which includes static data removal, time-zero correction, distance normalization, data filtering, and stacking. These steps were scripted and automated in R programming, excluding data filtering, which was used from an existing package, RGPR. The study compared profiles processed using GSSI software to profiles processed using the R script developed here to ensure comparable functionality and output. While an SME is currently still necessary for interpretations, this script eliminates the need for one to post-process GSSI GPR data.

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