This paper presents a novel artificial intelligence(AI)-assisted two-stage method for optimising rock slope stability by integrating advanced 3D modelling with rock support design,aiming at minimising risks,material u...This paper presents a novel artificial intelligence(AI)-assisted two-stage method for optimising rock slope stability by integrating advanced 3D modelling with rock support design,aiming at minimising risks,material usage,and costs.In the first stage,an extended key block analysis identifies key blocks and key block groups,accounting for progressive failure and force interactions.The second stage uses AI algorithms to optimise rockbolting design,balancing stability,cost,and material use.The most efficient algorithms include the multi-objective tree-structured Parzen estimator(MOTPE)and non-dominated sorting genetic algorithms(NSGA-II and NSGA-III).Applied to the Larvik rock slope,the optimised solution uses 18 pre-tensioned cablebolts,providing 13.2 MN of active force and achieving a factor of safety of 1.31 while reducing the average anchorage length by approximately 16%compared to traditional design.The AI-assisted approach also reduces computation time by over 90%compared to Quasi-Monte Carlo(QMC)methods,demonstrating its efficiency for small-scale civil engineering projects and large-scale mining operations.The developed tool is practical,compatible with Building Information Modelling(BIM),and ready for engineering implementation,supporting sustainable and cost-effective rock slope stabilisation.While the method is largely automated,professional judgement remains crucial for verifying ground conditions and selecting the final solution.Future work will focus on integrating data uncertainties,addressing complex block deformation mechanisms,refining optimisation objectives,and improving the performance of multi-objective optimisation for slope rockboling applications to further enhance the method's versatility.展开更多
基于实际项目,提出1种基于全息立体显示、实体电子沙盘和激光交互的虚拟仿真展示系统。系统由大型全息立体显示系统、实体电子沙盘系统、激光雕刻系统、6DOF(Six Degrees of Freedom,六自由度)光学定位系统和集控管理平台系统5个模块组...基于实际项目,提出1种基于全息立体显示、实体电子沙盘和激光交互的虚拟仿真展示系统。系统由大型全息立体显示系统、实体电子沙盘系统、激光雕刻系统、6DOF(Six Degrees of Freedom,六自由度)光学定位系统和集控管理平台系统5个模块组成,构建沉浸感知、动态演绎与实时交互相结合的混合现实平台。测试表明系统实现了真3D精细可视化(立体显示深度误差<0.5%)、低延迟高精度交互(渲染延迟28.7 ms,多用户协同交互正确率98.6%)以及72 h稳定连续运行,各项指标均达到设计要求。该系统的应用显著提升了露天矿山数字化成果的立体展示与交互能力,为露天煤矿智慧化建设成果的可视化呈现和管理决策提供了有力支撑。展开更多
基金support from Research Council of Norway via STIPINST PhD grant(Grant No.323307),Bever Control AS,and Bane NOR.
文摘This paper presents a novel artificial intelligence(AI)-assisted two-stage method for optimising rock slope stability by integrating advanced 3D modelling with rock support design,aiming at minimising risks,material usage,and costs.In the first stage,an extended key block analysis identifies key blocks and key block groups,accounting for progressive failure and force interactions.The second stage uses AI algorithms to optimise rockbolting design,balancing stability,cost,and material use.The most efficient algorithms include the multi-objective tree-structured Parzen estimator(MOTPE)and non-dominated sorting genetic algorithms(NSGA-II and NSGA-III).Applied to the Larvik rock slope,the optimised solution uses 18 pre-tensioned cablebolts,providing 13.2 MN of active force and achieving a factor of safety of 1.31 while reducing the average anchorage length by approximately 16%compared to traditional design.The AI-assisted approach also reduces computation time by over 90%compared to Quasi-Monte Carlo(QMC)methods,demonstrating its efficiency for small-scale civil engineering projects and large-scale mining operations.The developed tool is practical,compatible with Building Information Modelling(BIM),and ready for engineering implementation,supporting sustainable and cost-effective rock slope stabilisation.While the method is largely automated,professional judgement remains crucial for verifying ground conditions and selecting the final solution.Future work will focus on integrating data uncertainties,addressing complex block deformation mechanisms,refining optimisation objectives,and improving the performance of multi-objective optimisation for slope rockboling applications to further enhance the method's versatility.
