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Deep learning-based identification of rock discontinuities on 3D model of tunnel face
Tunnelling and Underground Space Technology ( IF 6.7 ) Pub Date : 2025-01-17 , DOI: 10.1016/j.tust.2025.106403
Chuyen Pham, Byung-Chan Kim, Hyu-Soung Shin

Discontinuity mapping on tunnel faces is crucial for assessing stability and determining the need for additional reinforcement during tunnel construction. The traditional manual mapping approach is time-consuming and error-prone, necessitating a more accurate and efficient approach. This study explores a novel approach using photogrammetry to reconstruct digital 3D models of tunnel faces, enabling comprehensive discontinuity characterization without any time restriction. Despite challenges in image data collection and processing procedures, photogrammetry proves to be a viable alternative to LiDAR scanning for reconstructing precise 3D models of tunnel faces. Additionally, a deep learning technique is proposed to automatically identify rock mass discontinuities departing from massive random fractures on the 3D tunnel face. Since working directly with 3D models in deep learning is still challenging, the 3D tunnel face model is projected into four 2D images (i.e., RGB, depth map, normal vector, and curvature images) encompassing all necessary information of the 3D model. Afterward, a 2D semantic segmentation deep learning model is trained to identify areas of discontinuity based on the projected multi-2D images. Finally, the identified discontinuities are re-projected onto the 3D model to accurately reflect their original 3D context. Our results indicate that the proposed approach not only automatically and accurately quantifies rock discontinuities but also minimizes subjectivity inherent in manual judgment.

中文翻译:


基于深度学习的隧道掌子面三维模型岩石不连续面识别



在隧道施工期间,隧道掌子面的不连续性测绘对于评估稳定性和确定是否需要额外加固至关重要。传统的手动映射方法非常耗时且容易出错,因此需要一种更准确、更高效的方法。本研究探索了一种使用摄影测量法重建隧道掌子面数字 3D 模型的新方法,从而能够不受任何时间限制地进行全面的不连续性表征。尽管在图像数据收集和处理程序方面存在挑战,但摄影测量被证明是 LiDAR 扫描的可行替代方案,用于重建隧道掌子面的精确 3D 模型。此外,还提出了一种深度学习技术来自动识别 3D 隧道掌子面上大量随机裂缝的岩体不连续性。由于在深度学习中直接使用 3D 模型仍然具有挑战性,因此 3D 隧道面模型被投影为四个 2D 图像(即 RGB、深度图、法向量和曲率图像),其中包含 3D 模型的所有必要信息。之后,训练 2D 语义分割深度学习模型,以根据投影的多 2D 图像识别不连续区域。最后,将识别出的不连续性重新投影到 3D 模型上,以准确反映其原始 3D 环境。我们的结果表明,所提出的方法不仅可以自动准确地量化岩石不连续性,还可以最大限度地减少人工判断中固有的主观性。
更新日期:2025-01-17
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