| Nowadays, drawing up plans to control and manage infrastructural assets has become one of the most important challenges in most developed countries. The latter must cope with issues relating to the aging of their infrastructures, which are getting towards the end of their useful life. This study proposes an automatic approach for tunnel defects classification. Starting from non-destructive investigations using Ground Penetrating Radar (GPR), the deep convolutional neural networks (CNNs), with and without the application of bidimensional Fourier Transform (2D FT), have allowed the classification of several structural defects (e.g., crack, voids, anomaly, etc.) with high accuracy. The proposed methodology eliminates the need for human interpretation of Ground Penetrating Radar profiles and the use of integrative investigations (e.g., video-endoscopy, core drilling, jacking, and pull-out testing) for defects classification. As a result, it has significant speed and reliability that make it both time and cost-efficient. |
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