GPR-based detection of internal cracks in asphalt pavement: A combination method of DeepAugment data and object detection

Liu, Zhen; Gu, Xingyu; Wu, Wenxiu; Zou, Xiaoyong; Dong, Qiao; Wang, Lutai

doi:10.1016/j.measurement.2022.111281

Cited by 76 publications

(16 citation statements)

References 43 publications

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“…Typical distresses (potholes, cracks, loose, and poor interlayer) were identified using Faster R-CNN and YOLOv3 models. Results in the reference illustrated that the recognition precision of each algorithm can reach more than 80% [30]. Then, geometric and colour transformations were conducted to realize data enhancement by depth enhancement strategy.…”

Section: Methodsmentioning

confidence: 99%

Road detection and three-dimensional visualization management using deep learning and BIM integration

Liu,

2023

Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023)

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Asphalt pavement detection is a problem characterized by low efficiency, high manual input, and high cost. A 3D visualization management platform for road infrastructures based on artificial intelligence and information technology is presented in this study. Initially, images of cracks and potholes were processed digitally and segmented using image processing methods. A pavement condition index (PCI) is calculated by combining pavement distress identification with cracks and potholes. After that, ground penetrating radar (GPR) was used to detect the road structures, and then Canny edge algorithm was performed to process the original GPR images. Cracks, looseness, and poor interlayering were identified with the object detection model and data enhancement methods. Inverse time migration (3D) imaging of the internal diseases was performed in three dimensions (3D). Based on the results of the object detection model, F1 score and mAP were both above 80% for road distress detection. As a final step, a BIM model of asphalt pavement was generated using Revit software, and 3D asphalt pavement model rendering and quality details were displayed in Web-BIM browser, suggesting that this method can be used to maintain highways in a fast, intelligent, and scientific manner.

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Section: Methodsmentioning

confidence: 99%

Road detection and three-dimensional visualization management using deep learning and BIM integration

Liu,

2023

Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023)

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“…Then, the internal pavement condition index (IPCI) was used to evaluate the integrity of different structural layers of pavement, as shown in Equation (7). The distress characteristics were identified through GPR detection images of the longitudinal section of the road, according to the judgment results in previous studies [32][33][34][35]48]. In this study, the three most prevalent distresses were detected in this road section: cracks, looseness, and interlayer separation (IS).…”

Section: Gpr Investigationmentioning

confidence: 98%

“…In addition, ground-penetrating radar (GPR) has been widely used for the rapid nondestructive testing of pavement structural integrity because of its advantages regarding it high precision and good continuity [32][33][34][35]. GPR is a geophysical method used to detect the physical properties and spatial distribution of a medium by transmitting an antenna signal and receiving a high-frequency electromagnetic wave reflection signal.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Pavement Structural Conditions and Remaining Life Combining Accelerated Pavement Testing and Ground-Penetrating Radar

Liu,

Yang,

2023

Remote Sensing

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The inspection and monitoring of structural conditions are crucial for the maintenance of semi-rigid base pavement. To achieve the inverse calculation of material parameters and obtain the mechanical response of asphalt pavement, a method of modulus correction by reducing the error between tested and simulated strains was first developed. The relationship between the temperature at various depths within the pavement structure and atmospheric temperature was effectively demonstrated using a dual sinusoidal regression model. Subsequently, pavement monitoring data illustrated that as loading weight and temperature increased and loading speed decreased, the three-way strain of the asphalt layer increased. Thus, the relationship model between loading conditions and three-way strain was established with a good fitting degree (R2 > 0.95). The corrected modulus was obtained by approximating the error between simulated and measured strains. Then, the finite element analysis was performed to calculate key mechanical index values under various working conditions and predict the fatigue life of asphalt and base layers. Finally, ground-penetrating radar (GPR) detection was performed, and the internal pavement condition index was defined for quantitative assessment of structure conditions. The results show that there is a good correlation between the internal pavement condition index (IPCI) and remaining life of pavement structure. Therefore, our works solve the problems of the parameter reliability of pavement structures and quantitative assessment for structural conditions, which could support the performance prediction and maintenance analysis on asphalt pavement with a semi-rigid base.

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“…As an important part of urban transportation [14][15][16][17] , underground interchanges have solved the problem of large land occupation and difficult emission control of road intersections [18] , but the issue of traffic noise inside them cannot be ignored. Compared with traditional highway tunnels, underground interchanges have differences in traffic composition, with a higher proportion of cars and buses in underground interchanges and a higher proportion of trucks in highway tunnels [19] .…”

Section: Introductionmentioning

confidence: 99%

Study on measurement and simulation of noise field between long tunnel and underground interchange

Hong,

Liu,

2024

Fourth International Conference on Smart City Engineering and Public Transportation (SCEPT 2024)

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GPR-based detection of internal cracks in asphalt pavement: A combination method of DeepAugment data and object detection

Cited by 76 publications

References 43 publications

Road detection and three-dimensional visualization management using deep learning and BIM integration

Road detection and three-dimensional visualization management using deep learning and BIM integration

Assessment of Pavement Structural Conditions and Remaining Life Combining Accelerated Pavement Testing and Ground-Penetrating Radar

Study on measurement and simulation of noise field between long tunnel and underground interchange

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