Automatic Classification of Pavement Distress Using 3D Ground-Penetrating Radar and Deep Convolutional Neural Network

Liang, Xingmin; Yu, Xin; Chen, Chen; Jin, Yong; Huang, Jiandong

doi:10.1109/tits.2022.3197712

Cited by 26 publications

(9 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Figure 13 suggests that the maximum shear strain of the asphalt layer of a semi-rigid pavement ranged from 5.53 µε to 133. 48 as follows: the maximum tension strain under the most unfavorable and favorable working conditions was 164.39 με and 5.44 με, respectively. The maximum compression strain under the most unfavorable and favorable working conditions was 1210.36 με and 82.73 με, respectively.…”

Section: Prediction Results Of Fatigue Failure and Critical Conditionsmentioning

confidence: 99%

“…Figure 13 suggests that the maximum shear strain of the asphalt layer of a semi-rigid pavement ranged from 5.53 με to 133. 48 The FE calculation results in Figure 14 demonstrate that the tension stress at the bottom of the base layer was between 0.101 MPa and 0.237 MPa under different working conditions. Combined with Equation ( 17), it could be calculated that the Nf2 of the base layer is between 5.33 × 10 8 and 4.60 × 10 10 equivalent-axle times.…”

Section: Prediction Results Of Fatigue Failure and Critical Conditionsmentioning

confidence: 99%

“…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%

See 2 more Smart Citations

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

Liu,

Yang,

2023

Remote Sensing

View full text Add to dashboard Cite

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.

show abstract

Section: Prediction Results Of Fatigue Failure and Critical Conditionsmentioning

confidence: 99%

Section: Prediction Results Of Fatigue Failure and Critical Conditionsmentioning

confidence: 99%

Section: Gpr Investigationmentioning

confidence: 98%

See 1 more Smart Citation

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

Liu,

Yang,

2023

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Using 2D grid images composed of multiple vertical and horizontal slices as input data for deep CNN, Kim et al classified cavities, pipes, and rainwater wells in Seoul, South Korea [24]. Liang et al used VGG16 combined with ResNet50 to train the above model using 2525 images of 3D GPR containing four types of images: cracked, connected, cavity and loose [25]. The limitation of their study is the insufficient amount of data with only 2525 images, after splitting into training and test data.…”

Section: Introductionmentioning

confidence: 99%

3D ground penetrating radar cavity identification algorithm for urban roads using transfer learning

Li¹,

Yang²,

Qiao³

et al. 2023

Meas. Sci. Technol.

View full text Add to dashboard Cite

3D ground penetrating radar (GPR) is the main method for the detection of underground cavities in urban roads. The number of road cavity samples detected by 3D radar is small, whereas the intelligent identification model require a large number of learning samples for model training, resulting in inadequate model training. This causes the model to be less accurate in identifying cavities, resulting in many misses and misjudgments. Given the above problems, combined with the detection characteristics of the vertical, the horizontal, and the crossed slices obtained in one detection process of 3D GPR, a 3D GPR cavity intelligent recognition model based on model-based transfer learning is proposed. Firstly, a large amount of 3D GPR data of urban road models with cavities are obtained through forwarding simulation. And the intelligent recognition model was pre-trained on the cavity detection data on three types of slices respectively. Then, through model-based transfer learning, a small amount of real underground cavity data is used to speed up the convergence speed of model training and optimize the structural parameters. It breaks through the limitation of the insufficient number of cavity samples for 3D radar detection on the intelligent learning model training, reduces algorithm training costs, and improves identification accuracy.

show abstract

“…Most of the earliest approaches [15,16,17] relied on threshold processing algorithms that assumed the crack pixel was darker than its neighbors. The threshold segmentation approach was frequently utilized in early image segmentation techniques because of its ease of use and speed.…”

Section: Introductionmentioning

confidence: 99%

Detection of Road Cracks Using Convolutional Neural Networks and Threshold Segmentation

Ashraf

Sophian

Shafie

et al. 2022

JIAE

View full text Add to dashboard Cite

Automatic road crack detection is an important transportation maintenance responsibility for ensuring driving comfort and safety. Manual inspection is considered to be a risky method because it is time consuming, costly, and dangerous for the inspectors. Automated road crack detecting techniques have been extensively researched and developed in order to overcome this issue. Despite the difficulties, most of the proposed methodologies and solutions involve machine vision and machine learning, which have lately acquired traction largely due to the increasingly more affordable processing power. Nonetheless, it remains a difficult task due to the inhomogeneity of crack intensity and the intricacy of the background. In this paper, a convolutional neural network-based method for crack detection is proposed. The method is inspired from recent advancements in applying machine learning to computer vision. The primary goal of this work is to employ convolutional neural networks to detect the road crack. Data in the form of images has been used as input, preprocessing and threshold segmentation is applied to the input data. The processed output is feed to CNN for feature extraction and classification. The training accuracy was found to be 96.20 %, the validation accuracy to be 96.50 %, and the testing accuracy to be 94.5 %.

show abstract

Automatic Classification of Pavement Distress Using 3D Ground-Penetrating Radar and Deep Convolutional Neural Network

Cited by 26 publications

References 29 publications

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

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

3D ground penetrating radar cavity identification algorithm for urban roads using transfer learning

Detection of Road Cracks Using Convolutional Neural Networks and Threshold Segmentation

Contact Info

Product

Resources

About