知识图谱与地灾风险势场协同驱动复杂山区铁路线路优化OA

Disaster-mitigation-oriented railway alignment design is crucial for ensuring safety throughout the entire life cycle of railway projects,particularly in complex mountainous areas.However,two major challenges remain.First,the expertise accumulated through conventional manual alignment design is largely stored in unstructured forms and is therefore difficult to incorporate directly into intelligent optimization programs,resulting in a loss of expert knowledge in automated alignment design systems.Second,conventional geohazard risk quantification generally treats risk as a scalar,neglecting the interactions among risk units within a region and their directional influence on railway alignments.Therefore,there is an urgent need to develop an intelligent railway alignment optimization framework that integrates structured knowledge representation with refined risk quantification,thereby facilitating the transfer and reuse of expert knowledge and the coordinated optimization of construction cost and geohazard risk.To this end,a railway alignment design ontology was first developed,based on which a Hazard-Reduction Railway Alignment Design Knowledge Graph(HRRAD-KG)was constructed to transform unstructured expert experience into machine-readable and queryable structured knowledge.Geological investigation data were then incorporated to establish a geohazard risk potential-field model.The model quantifies geohazard risk levels,uses potential-field energy to characterize hazard severity,and employs the direction of repulsive forces to dynamically guide the development of the railway alignment.Subsequently,subject to the constraints derived from the knowledge graph and based on the quantified results of the geohazard risk potential field,a bi-objective optimization model minimizing construction cost and geohazard risk was formulated.A distance-transform algorithm jointly driven by the knowledge graph and the risk potential field was proposed to solve the model.Finally,a real railway project in a complex mountainous area was selected to verify the effectiveness of the proposed method.The case study results show that the overall route of the machine-generated alignment solution jointly guided by the knowledge graph and the geohazard risk potential field is broadly consistent with that of the optimal manually designed alignment.Compared with the manually designed solution,the machine-generated solution reduces construction cost and geohazard risk by 11.4%and 6.4%,respectively.The findings provide a technical basis for improving the construction-and operation-stage safety of railways in geohazard-prone complex mountainous areas from the earliest planning and alignment design stages.