Sequential Identification of Rock Mass Discontinuities from Tunnel Face Point Clouds: A Dynamic Update Framework for Digital Twins
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Accurate characterization of rock mass discontinuities is critical for stability analysis and support design in underground tunnel construction. While 3D point cloud technology offers high-precision geometrical data, traditional identification methods predominantly focus on isolated tunnel faces, neglecting the longitudinal spatial correlations inherent in the continuous excavation process. This study introduces a dynamic identification framework that leverages sequential tunnel face point clouds to enhance discontinuity recognition. By utilizing previously excavated faces as prior spatial information, we employ a probabilistic tracking method to predict and refine the discontinuity parameters of the current face. This approach effectively distinguishes persistent geological discontinuities from random, blast-induced fractures, significantly reducing false positives. The proposed framework facilitates the construction of high-fidelity Discrete Fracture Network (DFN) models and supports accurate rock mass classification. This research contributes to the development of geotechnical digital twins by providing a continuous, evolving geometric representation of the tunnel environment, ensuring safer and more efficient construction management.
