GPR monitoring for road transport infrastructure: A systematic review and machine learning insights

Rasol, Mezgeen; Pais, Jorge C.; Pérez-Gracia, Vega; Solla, Mercedes; Fernandes, Francisco Manuel Carvalho Pinto; Fontul, Simona; Ayala-Cabrera, David; Schmidt, Frank; Assadollahi, Hossein

doi:10.1016/j.conbuildmat.2022.126686

Cited by 83 publications

(25 citation statements)

References 120 publications

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“…Ground Penetrating Radar (GPR) is a geophysical tool used for the survey of underground features in archaeology [1][2][3][4][5], the prospection and the state of conservation assessment in historical buildings [6][7][8], as well as in other research and professional fields, such as geology and civil engineering [9][10][11][12]. It can detect underground structures, canalizations, burials, and tentatively stratigraphic discontinuities in soil [13].…”

Section: Introductionmentioning

confidence: 99%

A Newly Developed Tool for the Post-Processing of GPR Time-Slices in A GIS Environment

2022

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Ground-penetrating radar (GPR) is a precious and reliable research tool broadly used in archaeology because of its capacity to produce three-dimensional data about features preserved underground, such as buildings, infrastructures, and burials, as well as building rubble. GPR data (time-slices) management and exploitation in Geographic Information Systems (GIS) is mostly limited to the visualization and the manual interpretation and mapping of separate single time-slices. This study presents a newly developed plug-in designed to automatically post-process GPR time-slices in a GIS environment, to identify anomalies, and to produce a synchronic view of them. This map product, when combined with a DTM, results in a 2D map of subsurface anomalies which shows the absolute height of features above sea level, thus offering a comprehensive view of the three-dimensional configuration of the subsurface features identified. The paper illustrates the pixel-based processing chain of the plug-in and the results of the tests carried out in the case study of the Roman town of Falerii Novi (Italy), on the basis of high-resolution open access GPR data recently collected by the University of Cambridge and Ghent.

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Section: Introductionmentioning

confidence: 99%

A Newly Developed Tool for the Post-Processing of GPR Time-Slices in A GIS Environment

2022

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“…Thus, ground-penetrating radar can be used for the identification of internal defects in bridge decks, the layout of reinforcement. 111,112 Ground-penetrating radar is better suited to detecting defects deeper inside than infrared thermography. For ground-penetrating radar data, the single point test data is a vector.…”

Section: Ground Penetrating Radar-oriented Methodsmentioning

confidence: 99%

Review of artificial intelligence-based bridge damage detection

Zhang

Yuen

2022

Advances in Mechanical Engineering

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Bridges are often located in harsh environments and are thus extremely susceptible to damage. If the initial damage is not detected in time, it can develop further causing safety hazards. Therefore, accurate detection of bridge damage is an important topic. In recent years, artificial intelligence technology has been developed rapidly, especially machine learning algorithms, which have shown amazing results in various fields while it also received attention in bridge inspection. This paper summarizes the progress of bridge damage detection research related to artificial intelligence techniques between 2015 and 2021. For structural health monitoring, sensing data is the basis for various data processing methods. The strength and weakness of the sensing data itself directly affect the effectiveness of subsequent processing methods. As a result, this paper classifies bridge damage detection studies into six categories from the types of sensing data: visual image, point cloud, infrared thermal imaging, ground-penetrating radar, vibration response, and other types of data. These six types of damage detection methods were reviewed and summarized respectively. Finally, challenges and future trends were discussed.

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“…It must consider the effect of the overlay on the concrete layer's load-induced fatigue stress and temperature-induced fatigue stress. Equations ( 8) and ( 13) are adjusted by Formulas (20) and (21).…”

Section: Determination Of Model Parametersmentioning

confidence: 99%

“…A review of the literature indicates that surface functional failure does not have a simple relationship with pavement inner condition [19][20][21][22][23]. The predicted functional remaining life of an in-service pavement with a recent new overlay may be seriously overestimated.…”

Section: Introduction 1backgroundmentioning

confidence: 99%

A Fast and Non-Destructive Prediction Model for Remaining Life of Rigid Pavement with or without Asphalt Overlay

et al. 2022

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Remaining life is an important indicator of pavement residual effective service time and is directly related to maintenance decision-making with limited funds. This paper proposes a fast and non-destructive model to predict the remaining life of rigid PCC (Portland cement concrete) pavement, with or without asphalt overlay. Firstly, a model was constructed according to the current Chinese design specifications for concrete pavement integrating an inverse design concept. Secondly, the prediction model was applied to three typical pavement sections with 1430, 1250 and 1000 slabs, respectively. Ground penetrating radar (GPR) was utilized to determine the geometric parameters in the predictive model and the physical state of the pavement. A falling weight detector (FWD) was utilized for determination of the mechanical parameters. A more reasonable equivalent elastic modulus of foundation was back-calculated instead of using the limited model in the design specification. Thirdly, the remaining life was predicted based on the current mechanical and geometric parameters. The distributions of the remaining life of the three pavement sections was statistically analyzed. Finally, a decision-making system to inform maintenance strategy was proposed based on the remaining life and the technical condition of each slab. The results showed that the relationship between the remaining life and the mechanical parameters, geometric parameters and the physical state of the pavement was highly consistent with engineering experience. The success rate of the prediction model was as high as 96%. The proposed fast and non-destructive prediction model showed good engineering applicability and feasibility. The decision-making system was shown to be feasible in terms of economic benefits.

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GPR monitoring for road transport infrastructure: A systematic review and machine learning insights

Cited by 83 publications

References 120 publications

A Newly Developed Tool for the Post-Processing of GPR Time-Slices in A GIS Environment

A Newly Developed Tool for the Post-Processing of GPR Time-Slices in A GIS Environment

Review of artificial intelligence-based bridge damage detection

A Fast and Non-Destructive Prediction Model for Remaining Life of Rigid Pavement with or without Asphalt Overlay

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