The traditional two-dimensional design method for the side and front slopes of tunnel portal projects face some challenges,such as difficulties in quality control,low parameterization and automation,and a lack of spatial relationship.To address these problems,a design scheme for tunnel portal slopes based on parametric,three-dimensional,and intelligent concepts is proposed.Initially,a database of tunnel portal slope design cases is established;further,a deep learning algorithm is employed to intelligently determine the portal boundary mileage and excavation parameters of the tunnel portal slope.Subsequently,a slope transition mode based on a curved surface is introduced,a swarm intelligence algorithm is applied to optimize the selection of controlling transition sections,and excavation control points are estimated to stitch them into a mesh,thereby establishing a slope excavation model.Finally,the integrated slope excavation model and terrain model are subjected to Boolean operations to determine the boundary and quantities of excavation accurately and automatically.A case study conducted on the Changsha-Ganzhou railway using the proposed method reveals that compared with the traditional method,the design scheme obtained via intelligent recommendations exhibits high reliability,a reduction in the excavation boundaries with notable distortion by 28.0%,and an increase in the average accuracy rate of excavation quantities by 8.7%.These results confirm that the proposed method improves the efficiency and quality of the tunnel portal slope design.
tunnel portal designside and front slopesparametric designintelligentizationdeep learning
万方数据
维普
