摘要
为了客观准确地评估铁路弃渣场的综合风险,建立基于改进投影寻踪聚类(PPC)模型的风险评价模型。首先建立了6个维度共19个因素的指标体系,并制定相应评价标准;然后借鉴K均值聚类思想来确定PPC模型关键系数-密度窗宽R,以解决传统方法造成的聚类效果差等问题;同时将遗传算法(GA)作为模型优化算法得到最优投影方向和样本投影值等关键数据;最后以贵昆线铁路的10座弃渣场作为案例研究,研究结果表明:“弃渣场边坡条件”和“地形与地基条件”2个维度对弃渣场的综合风险影响最大;验证了所建模型的科学性,评价结果能够规避人为主观因素的干扰,更加与实际情况相贴合。
In order to objectively and accurately evaluate the comprehensive risk of railway abandoned dreg site,a risk assessment model based on the improved projection pursuit clustering (PPC) model was established.Firstly,an index system involving 19 factors in 6 dimensions was established,and the corresponding evaluation criteria were formulated.Then,the key coefficient of PPC model,the window radius R of local density,was determined by using the K-means clustering method,so as to solve the problems such as poor clustering effect caused by traditional methods.At the same time,the genetic algorithm (GA) was used as the model optimization algorithm to obtain the key data such as the optimum projection direction and sample projection value.Finally,ten abandoned dreg sites of Guizhou-Kunming railway were used as the case studies.The results showed that the two dimensions of “slope condition” and “topography and foundation condition” had the greatest influence on the comprehensive risk of abandoned dreg site.The scientific nature of the established model was verified.The evaluation results could avoid the interference of human subjective factors and were more in line with the actual situation.
作者
吴伟东
苟唐巧
许博浩
潘海泽
何鑫
WU Weidong;GOU Tangqiao;XU Bohao;PAN Haize;HE Xin(School of Civil Engineering and Architecture,Southwest Petroleum University,Chengdu Sichuan 610500,China)
出处
《中国安全生产科学技术》
CAS
CSCD
北大核心
2019年第8期181-186,共6页
Journal of Safety Science and Technology
基金
国家自然科学基金项目(51574201)
关键词
铁路弃渣场
风险评价
改进投影寻踪聚类(PPC)模型
遗传算法(GA)
投影方向
railway abandoned dreg site
risk assessment
improved projection pursuit clustering (PPC) model
genetic algorithm (GA)
projection direction
