Subway shield tunnels have emerged as the preferred solution for urban transportation due to their convenience and safety. Constructed using prefabricated concrete segments, these tunnels exhibit structural stability. However, the segment joints and bolt holes are prone to groundwater infiltration under prolonged external stress, potentially compromising the lifespan of the shield tunnels. Consequently, effective detection methods are imperative to ensure the safe operation of these tunnels. Accurate data acquisition and precise extraction of leakage features are critical for detecting leakages in subway tunnels. This research introduces Efficient Geometry Enhanced Point Transformer Network (GEPT-Net), an innovative point cloud semantic segmentation network designed specifically for detecting tunnel leakage. GEPT-Net leverages the observation that leakages predominantly occur at segment joints and bolt holes, characterized by distinct geometric features and lower intensity. The network incorporates Fast Point Feature Histograms (FPFH) to effectively capture these geometric features from the input data. Additionally, we introduce a point cloud serialization technique utilizing space-filling curves, enabling the network to perceive a greater number of points within the same computational power, thereby balancing efficiency and accuracy. The Geometry Enhanced Channel Attention (GECA) Block is introduced to enhance the interaction between FPFH feature channels and intensity channels, enhancing the precise localization of leakage areas. Furthermore, the Lovasz Hinge Loss is employed to address the issue of extreme class imbalance. We constructed a tunnel leakage point cloud dataset, named S3DIS_leakage, comprising approximately 1,600 m between two stations, to train and evaluate the performance of our network. Experimental results demonstrate that GEPT-Net achieves superior performance in tunnel leakage semantic segmentation, attaining approximately 85 % mean Intersection over Union and 89 % accuracy for leakage classes, outperforming cutting-edge 2D and 3D networks by at least 12 %. Moreover, GEPT-Net maintains a remarkable balance between segmentation accuracy and computational efficiency, rendering it viable for practical engineering applications. This study not only establishes a robust approach for tunnel leakage detection but also paves the way for future research on the comprehensive segmentation of shield tunnel components. The proposed framework is available from the following github repository: https://github.com/jdjiang312/GEPT-Net.

中文翻译：

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GEPT-Net：用于盾构隧道泄漏分段的高效几何增强点变压器


地铁盾构隧道因其便利性和安全性而成为城市交通的首选解决方案。这些隧道使用预制混凝土管片建造，具有结构稳定性。然而，管片接头和螺栓孔在长时间的外部应力下容易被地下水渗透，从而可能影响盾构隧道的使用寿命。因此，有效的检测方法对于确保这些隧道的安全运行至关重要。准确的数据采集和泄漏特征的精确提取对于检测地铁隧道中的泄漏至关重要。本研究引入了高效几何增强点变压器网络 （GEPT-Net），这是一种专为检测隧道泄漏而设计的创新点云语义分割网络。GEPT-Net 利用了泄漏主要发生在节段接头和螺栓孔的观察结果，其特征是明显的几何特征和较低的强度。该网络包含快速点特征直方图 （FPFH），以有效地从输入数据中捕获这些几何特征。此外，我们引入了一种利用空间填充曲线的点云序列化技术，使网络能够在相同的计算能力内感知更多的点，从而平衡效率和准确性。引入几何增强通道注意 （GECA） 模块以增强 FPFH 特征通道和强度通道之间的交互，从而增强泄漏区域的精确定位。此外，Lovasz 铰链损失用于解决极端阶级不平衡的问题。 我们构建了一个名为 S3DIS_leakage 的隧道泄漏点云数据集，包括两个站点之间大约 1,600 m 的距离，以训练和评估我们网络的性能。实验结果表明，GEPT-Net 在隧道泄漏语义分割方面取得了卓越的性能，在接头上实现了大约 85% 的平均交集和 89% 的泄漏类别准确率，比尖端的 2D 和 3D 网络至少高出 12%。此外，GEPT-Net 在分割精度和计算效率之间保持了显著的平衡，使其适用于实际工程应用。这项研究不仅建立了一种稳健的隧道泄漏检测方法，而且为未来盾构隧道组件综合分割的研究铺平了道路。建议的框架可从以下 github 存储库获得：https://github.com/jdjiang312/GEPT-Net。