Optimization of railway alignments in regions with multiple constraints based on a 3D rapidly exploring random tree algorithm
Railway design presents a complex engineering challenge,particularly in areas with undulating terrains and numerous obstacles,in the study areas. Existing computer-aided alignment optimization methods often require extensive computation time and resources to generate an optimized alignment and are even prone to stagnate and fail to find a feasible alignment. To address these issues,a 3D rapidly exploring random tree algorithm was proposed for rapidly developing feasible alignments that satisfy all the constraints. First,a random tree heuristic sampling method termed as the horizontal-vertical integrated sampling method was proposed for avoiding the random tree search process falling into a local optimal. This sampling method extends the random tree search to three-dimensional spaces and achieves a comprehensive exploration of the entire study area. Second,to efficiently retrieve relevant environmental information during the alignment search process,a unified management method for multi-source heterogeneous integrated geographic information was proposed. Specifically,based on the characteristics of different environmental information,such as terrains and obstacles,specific storage strategies were designed for discretizing them into a comprehensive geographic information model. Then,this environmental information was capable to be dynamically checked during alignment search process. Afterward,the random tree evolutionary search method was obtained by integrating the heuristic sampling method and the obstacle handling operator. This method can efficiently retrieve and handle relevant obstacles during search process as well as to rapidly generate optimized path solution. Ultimately,the proposed method was applied to a real-world case in a study area with multiple constraints. The experimental results reveal that this method can effectively bypass all the obstacle constraints and rapidly generate an optimized alignment. As compared to the best manually designed alignment solution,the optimized alignment derived from the proposed method achieves 4.8％ lower construction cost. The experimental results show that this method improves alignment design efficiency and provides valuable references for human designers.
railway designalignment optimizationheuristic sampling processrapidly exploring random tree algorithmconstrained optimization
万方数据
维普
