A tunnel lining health evaluation method with ground-penetrating radar and deep learning

doi:10.13474/j.cnki.11-2246.2025.0321

Abstract

Abstract: Tunnel in its service life, affected by a variety of factors, behind the tunnel wall will produce a variety of structural diseases such as voids, incompact, and so on, affecting the service performance, the ground-penetrating radar (GPR) nondestructive testing technology is widely used in the field of tunnel quality inspection, but due to the complexity of the radar data deciphering work and the large amount of data, the detection efficiency needs to be improved. In recent years, machine learning has attracted much attention due to its excellent data processing capability and information extraction ability, providing a variety of efficient and reliable disease classification models. In this paper, based on ground-penetrating radar images, a multilevel disease classification method is proposed for assessing tunnel lining health. Firstly, in this paper, radar image data are acquired and manually decoded to create a sample database to be used as inputs and outputs of the model in order to train and test the deep learning model. For the small sample characteristics of the database, the data are classified using the Vision Transformer network and the improved Compact Convolutional Transformer. The results show that the Vision Transformer algorithm can achieve tunnel lining health evaluation based on radar images with better results and high accuracy compared to other versions.

Key words: ground penetrating radar, neural network, Vision Transformer, tunnel lining health evaluation

CLC Number:

P208

ZHANG Guangwei. A tunnel lining health evaluation method with ground-penetrating radar and deep learning[J]. Bulletin of Surveying and Mapping, 2025, 0(3): 122-126,149.

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