In recent years, great attention has focused on the development of automated procedures for infrastructures control. Many efforts have aimed at greater speed and reliability compared to traditional methods of assessing structural conditions. The paper proposes a multi-level strategy, designed and implemented on the basis of periodic structural monitoring oriented to a cost- and time-efficient tunnel control plan. Such strategy leverages the high capacity of convolutional neural networks to identify and classify potential critical situations. In a supervised learning framework, Ground Penetrating Radar (GPR) profiles and the revealed structural phenomena have been used as input and output to train and test such networks. Image-based analysis and integrative investigations involving video-endoscopy, core drilling, jacking and pull-out testing have been exploited to define the structural conditions linked to GPR profiles and to create the database. The degree of detail and accuracy achieved in identifying a structural condition is high. As a result, this strategy appears of value to infrastructure managers who need to reduce the amount and invasiveness of testing, and thus also to reduce the time and costs associated with inspections made by highly specialized technicians.

近年来，人们对基础设施控制自动化程序的开发给予了极大的关注。与评估结构条件的传统方法相比，许多努力旨在提高速度和可靠性。本文提出了一种多层次的策略，该策略是在定期结构监测的基础上设计和实施的，该监测面向具有成本和时间效益的隧道控制计划。这种策略利用卷积神经网络的高容量来识别和分类潜在的危急情况。在监督学习框架中，探地雷达 (GPR) 剖面和揭示的结构现象已被用作输入和输出来训练和测试此类网络。基于图像的分析和综合调查，包括视频内窥镜检查、岩心钻孔、已利用顶升和拉出测试来定义与 GPR 剖面相关的结构条件并创建数据库。识别结构条件的详细程度和准确性很高。因此，这种策略对于需要减少测试数量和侵入性的基础设施管理人员来说似乎很有价值，因此也需要减少与高度专业化技术人员进行检查相关的时间和成本。