Digital tunnel geometry model (DTGM): A multimodal data fusion framework for rock mass feature quantification

Abstract

Accurate acquisition of rock mass parameters in complex tunnel environments remains challenging due to limitations of conventional non-contact measurement techniques. This study introduces a novel digital tunnel geometry model (DTGM) framework designed to address the limitations associated with reliance on a single data source. By implementing multi-source data fusion of LiDAR and photogrammetric measurements, the DTGM achieves millimeter-level geometric accuracy in rock mass characterization. In the study, three innovative contributions are introduced: (1) an automated robust denoising algorithm for tunnel point clouds, (2) a surface reconstruction optimization algorithm emphasizing the preservation of rock mass morphological undulations and fracture structures, and (3) a parametric data fusion methodology coupling geometric models with rock mass attributes. Comparative analysis shows that the proposed method offers higher computational efficiency and reconstruction quality. Case studies validate the effectiveness of the proposed framework in acquiring critical rock mass parameters, showing 51 % (dip direction) and 58 % (dip angle) improvement in discontinuity analysis accuracy over traditional point cloud inputs. The quantification of fracture/trace plane parameters overcomes the inherent limitation of dimensional deficiency in image-based 3D modeling. Results establish the engineering superiority of the DTGM in rock mass parameter resolution and quantitative analysis while also advancing digital twin implementation through a novel virtual modeling paradigm.