隧道围岩超欠挖自注意力机制的高斯混合模型点云配准方法OA

The over-excavation and under-excavation values of surrounding rock are important indicators for tunnel construction quality and safety control.Traditional total station measurements of tunnel surrounding rock are inefficient for detecting over-and under-excavation.Three-dimensional(3D)laser scanning technology,however,enables efficient acquisition of spatial point cloud data.Nevertheless,conventional tunnel point cloud ellipse fitting methods are difficult to apply to the extraction of over-and under-excavation values in multi-center circular tunnel surrounding profiles.To address this issue,this paper proposes a point cloud registration method for tunnel surrounding rock over-and under-excavation based on a Gaussian mixture model(GMM)with a self-attention mechanism.The method first constructs a 3D model based on the tunnel design profile and transforms it into a design point cloud.Next,for the measured point cloud of the tunnel surrounding rock profile,voxel filtering and a density-based spatial clustering algorithm are applied for down-sampling and denoising.The adaptability of the method to tunnel surrounding rock point cloud profiles is verified,and optimal parameters are obtained.Finally,a Gaussian mixture model based on a self-attention mechanism is proposed.After registration of the design and measured point clouds,the method effectively extracts the over-and under-excavation values of the tunnel surrounding rock.The proposed method was successfully applied in the construction of the Tianshengqiao Tunnel on the Lincang-Shuangjiang Expressway in Yunnan.The maximum error in over-and under-excavation extraction was 2.01 mm,and the volume error percentage was 1.97％,meeting the precision requirements for engineering applications.This approach provides effective technical support for the rapid extraction of full cross-sectional over-and under-excavation values in tunnel surrounding rock.