In the last decades, the majority of the existing infrastructure heritage is approaching the end of its nominal design life mainly due to aging, deterioration, and degradation phenomena, threatening the safety levels of these strategic routes of communications. For civil engineers and researchers devoted to assessing and monitoring the structural health (SHM) of existing structures, the demand for innovative indirect non-destructive testing (NDT) methods aided with artificial intelligence (AI) is progressively spreading. In the present study, the authors analyzed the exertion of various deep learning models in order to increase the productivity of classifying ground penetrating radar (GPR) images for SHM purposes, especially focusing on road tunnel linings evaluations. Specifically, the authors presented a comparative study employing two convolutional models, i.e. the ResNet-50 and the EfficientNet-B0, and a recent transformer model, i.e. the Vision Transformer (ViT). Precisely, the authors evaluated the effects of training the models with or without pre-processed data through the bi-dimensional Fourier transform. Despite the theoretical advantages envisaged by adopting this kind of pre-processing technique on GPR images, the best classification performances have been still manifested by the classifiers trained without the Fourier pre-processing.

中文翻译：

---

具有和不具有傅里叶预处理的间接隧道监测的比较深度学习研究

在过去几十年中，大部分现有基础设施遗产正接近其标称设计寿命的终点，这主要是由于老化、退化和退化现象，威胁着这些战略通信路线的安全水平。对于致力于评估和监测现有结构的结构健康 (SHM) 的土木工程师和研究人员来说，对人工智能 (AI) 辅助的创新间接无损检测 (NDT) 方法的需求正在逐步扩大。在本研究中，作者分析了各种深度学习模型的应用，以提高为 SHM 目的对探地雷达 (GPR) 图像进行分类的生产率，尤其侧重于公路隧道衬砌评估。具体来说，作者介绍了一项比较研究，该研究采用了两种卷积模型，即 ResNet-50 和 EfficientNet-B0，以及最新的转换器模型，即 Vision Transformer (ViT)。准确地说，作者通过二维傅里叶变换评估了使用或不使用预处理数据训练模型的效果。尽管在 GPR 图像上采用这种预处理技术具有理论上的优势，但在没有进行傅里叶预处理的情况下训练的分类器仍然表现出最佳的分类性能。