Underground Object Classification for Urban Roads Using Instantaneous Phase Analysis of Ground-Penetrating Radar (GPR) Data

Park, Byeongjin; Kim, Jeongguk; Lee, Jaesun; Kang, Man-Sung; An, Yun‐Kyu

doi:10.3390/rs10091417

Cited by 49 publications

(32 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Regarding the GPR data processing and interpretation, follow-up works include the use of GPR phase analysis techniques [30] as an additional tool for the identification of cavities in corroded concrete. We also plan to explore the use of time-series clustering algorithms [31] to analyze profile traces in order to automate the identification and categorization of key features (such as cavities, rebars, and moist areas).…”

Section: Discussionmentioning

confidence: 99%

Assessing Rebar Corrosion through the Combination of Nondestructive GPR and IRT Methodologies

Solla

Lagüela

Fernández³

et al. 2019

Remote Sensing

View full text Add to dashboard Cite

Corrosion is one of the pathologies that most affects the resistance of reinforced concrete. There are numerous ancient structures still in use affected by corrosion that need proper evaluation and remedial treatment for their maintenance. In this sense, there has been an increasing tendency to use nondestructive testing techniques that do not alter the reinforcement elements of such vulnerable structures. This work presents a combined methodology by using ground penetrating radar (GPR) and infrared thermography (IRT) techniques for the detection and evaluation of corrosion. The methodology was applied to the case study of an old construction that belongs to the abandoned military battery of Cabo Udra (Galicia, Spain). The combination of these complementary techniques allowed for the identification of areas with different dielectric and thermal conductivity, as well as different reflection patterns and intensity of the GPR waves. Thus, from the analysis of the GPR signals and IRT images acquired, it was possible to interpret corroded areas and moisture, along with inner damages such as cracking and debonding. These pathologies have a direct effect on the durability and sustentation of a structure, while the knowledge of their existence might be useful for engineers engaged in the design of maintenance works.

show abstract

Section: Discussionmentioning

confidence: 99%

Assessing Rebar Corrosion through the Combination of Nondestructive GPR and IRT Methodologies

Solla

Lagüela

Fernández³

et al. 2019

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…If the relative permittivity of the underground object is lower than that of the surroundings, the reflected electromagnetic waves are in-phase with the radiated waves. Otherwise, the reflected electromagnetic waves will be indicated out-of-phase [16]. The phase change ratio of the extracted parabola boundary can be expressed by: Once the parabola boundary is automatically extracted, its phase can be analyzed as the final procedure.…”

Section: Development Of Ucnetmentioning

confidence: 99%

“…Since the electromagnetic waves propagating along the underground medium are dominantly reflected from the abrupt change of electromagnetic permittivity, the reflection signal features appear in the B-and C-scan images. To enhance visibility and detectability of the reflection signal features, a number of data processing techniques such as time-varying gain [13], subtraction [14,15], and basis pursuit-based background filtering [16] have been proposed. However, these techniques are highly susceptible to measurement noises especially in complex urban roads and sometimes unreliable due to decision making based on experts experiences.…”

Section: Introductionmentioning

confidence: 99%

“…Although the developed CNN using the combination of B-and C-scan images increased the classification accuracy compared to the conventional CNN using only B-scan images, it turned out that it still has difficulty differentiating underground cavities from chunks of gravel especially in complex urban areas due to their similar GPR reflection features. To address the misclassification issue caused by the underground chunks of gravel, Park et al recently proposed a phase analysis technique of GPR data [16]. However, the temporal and spatial resolutions of the 3D GPR data are often insufficient for the precise phase analysis.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

3D GPR Image-based UcNet for Enhancing Underground Cavity Detectability

Kang

Kim

Im³

et al. 2019

Remote Sensing

Self Cite

View full text Add to dashboard Cite

This paper proposes a 3D ground penetrating radar (GPR) image-based underground cavity detection network (UcNet) for preventing sinkholes in complex urban roads. UcNet is developed based on convolutional neural network (CNN) incorporated with phase analysis of super-resolution (SR) GPR images. CNNs have been popularly used for automated GPR data classification, because expert-dependent data interpretation of massive GPR data obtained from urban roads is typically cumbersome and time consuming. However, the conventional CNNs often provide misclassification results due to similar GPR features automatically extracted from arbitrary underground objects such as cavities, manholes, gravels, subsoil backgrounds and so on. In particular, non-cavity features are often misclassified as real cavities, which degrades the CNNs’ performance and reliability. UcNet improves underground cavity detectability by generating SR GPR images of the cavities extracted from CNN and analyzing their phase information. The proposed UcNet is experimentally validated using in-situ GPR data collected from complex urban roads in Seoul, South Korea. The validation test results reveal that the underground cavity misclassification is remarkably decreased compared to the conventional CNN ones.

show abstract

“…Ground-penetrating radar (GPR) is a nondestructive testing method that uses the penetrating ability of electromagnetic waves to determine the distribution of substances in an underground structure [1][2][3][4][5]. Forward modeling of GPR is used to simulate and analyze the electromagnetic response of an underground target by a numerical method, in order to obtain the reflected wave-shape of an underground target on the surface and to understand the propagation laws of the electromagnetic wave in an underground structure [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Analysis of GPR Wave Propagation Using CUDA‐Implemented Conformal Symplectic Partitioned Runge‐Kutta Method

2019

View full text Add to dashboard Cite

Accurate forward modeling is of great significance for improving the accuracy and speed of inversion. For forward modeling of large sizes and fine structures, numerical accuracy and computational efficiency are not high, due to the stability conditions and the dense grid number. In this paper, the symplectic partitioned Runge-Kutta (SPRK) method, surface conformal technique, and graphics processor unit (GPU) acceleration technique are combined to establish a precise and efficient numerical model of electromagnetic wave propagation in complex geoelectric structures, with the goal of realizing a refined and efficient calculation of the electromagnetic response of an arbitrarily shaped underground target. The results show that the accuracy and efficiency of ground-penetrating radar (GPR) forward modeling are greatly improved when using our algorithm. This provides a theoretical basis for accurately interpreting GPR detection data and accurate and efficient forward modeling for the next step of inversion imaging.

show abstract

Underground Object Classification for Urban Roads Using Instantaneous Phase Analysis of Ground-Penetrating Radar (GPR) Data

Cited by 49 publications

References 19 publications

Assessing Rebar Corrosion through the Combination of Nondestructive GPR and IRT Methodologies

Assessing Rebar Corrosion through the Combination of Nondestructive GPR and IRT Methodologies

3D GPR Image-based UcNet for Enhancing Underground Cavity Detectability

Analysis of GPR Wave Propagation Using CUDA‐Implemented Conformal Symplectic Partitioned Runge‐Kutta Method

Contact Info

Product

Resources

About