一种探地雷达与深度学习的隧道衬砌健康评价方法

doi:10.13474/j.cnki.11-2246.2025.0321

摘要/Abstract

摘要： 隧道在其服役期内,受多种因素影响,隧道壁后会产生空洞、不密实等多种结构病害,影响服役性能,探地雷达(GPR)无损检测技术广泛应用于隧道质量检测领域,但由于雷达数据的解译工作较为复杂,数据量大,检测效率有待提高。近年来,深度学习因其出色的数据处理能力和信息提取能力而备受瞩目,提供了多种高效、可靠的病害分类模型。本文基于GPR图像,提出了一种多级病害分类方法用于评估隧道衬砌健康状况。首先,获取雷达图像数据,并进行人工解译,创建样本数据库,用于模型的输入和输出,以训练和测试深度学习模型;然后,针对数据库的小样本特点,利用Vision Transformer网络和改进后的Compact Convolutional Transformer对数据进行分类。结果显示,Vision Transformer算法可以实现基于雷达影像的隧道衬砌健康评价,相较于其他版本,具有更好的结果及较高的准确率。

关键词: 探地雷达, 神经网络, Vision Transformer, 隧道衬砌健康评价

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

中图分类号:

P208

张广伟. 一种探地雷达与深度学习的隧道衬砌健康评价方法[J]. 测绘通报, 2025, 0(3): 122-126,149.

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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