Details

Autor(en) / Beteiligte

• Tong, Zheng
• Gao, Jie
• Zhang, Haitao

Titel

Recognition, location, measurement, and 3D reconstruction of concealed cracks using convolutional neural networks

Ist Teil von

• Construction & building materials, 2017-08, Vol.146, p.775-787

Ort / Verlag

Elsevier Ltd

Erscheinungsjahr

2017

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• •Project presents an application of convolutional neural networks (CNN) in cracks.•Different CNNs are established by the processes of structure design, training and testing.•The crack feature points are extracted by feature extraction CNN to establish 3D model.•CNN is able to recognize concealed cracks from other damage in GPR images with zero error.•CNNs could be accurately used for the recognition, location of concealed crack of asphalt pavement. Concealed cracks in asphalt pavement are the cracks that originate below the surface of the pavement. These cracks are a major contributing factor to pavement damage, in addition to being a major contributing factor to the formation of reflection cracks. The detection of a concealed crack is considered challenging because the location of the crack is, by definition, difficult to find. Therefore, the research on the utilization of ground penetrating radar (GPR) to locate concealed cracks has gained significant interest in recent years. However, the manually processed GPR image used for the recognition, location, and measurement of concealed cracks is inefficient and inaccurate. This project presents an application of convolutional neural networks (CNNs) to GPR images that automatically recognizes, locates, measures, and produces a 3D reconstruction of concealed cracks. In this project, three different CNNs (recognition, location, and feature extraction) were established to accomplish the aforementioned tasks automatically. Each CNN is developed through processes of structural design, training, and testing. The recognition CNN was designed to distinguish concealed cracks from other types of damage in a GPR image, the location CNN determined the location and length measurement of concealed crack images based on the results provided by the recognition CNN, and crack feature points were extracted by the feature extraction CNN to establish the 3D reconstruction models of the concealed cracks. The 3D reconstruction models were then used to calculate crack volume and predict the growth tendency of cracks. The results indicated that the recognition CNN is able to distinguish concealed cracks from other types of damages in 6482 GPR images with zero errors. In addition, the length recognition results calculated from the location CNN possess a 0.2543cm mean squared error, a 0.978cm maximum length error, and a 0.504cm average error in the test samples. Meanwhile, the feature extraction CNN is able to provide feature points for a 3D reconstruction model. The results of this study suggest that the CNNs could be accurately used for the recognition, location, and 3D reconstruction of concealed cracks in asphalt pavement in real-world applications.