In the context of large-scale hydropower infrastructure, the accurate extraction of tunnel rock mass structural information is vital for informed engineering design. However, this task remains inherently difficult due to the complexity of subsurface geological conditions and the inherent limitations of conventional survey techniques. This study proposes an integrated methodology that combines high-resolution 3D reconstruction with the Geo-AINet ensemble learning framework to enable automated identification of structural planes. The process involves generating detailed tunnel models, extracting multi-dimensional semantic features, performing initial segmentation, and subsequently applying cluster analysis to refine the structural interpretations. Experimental validation confirms the method’s high level of precision: the extracted structural orientations exhibit average angular deviations of less than 3 degrees, with a maximum error margin not exceeding 5 degrees relative to manual measurements. These findings demonstrate the method’s capacity to meet stringent engineering standards while enhancing both the efficiency and safety of geological data acquisition in tunneling projects.
Zhang et al. (Thu,) studied this question.