中南大学学报（自然科学版）2017，Vol.48Issue(5)：1268-1275,8.DOI:10.11817/j.issn.1672-7207.2017.05.020
基于粗糙集理论和支持向量机的岩爆预测
Rockburst prediction based on rough set theory and support vector machine
摘要
Abstract
In order to improve the accuracy of rockburst prediction in different environments and geologies, on the basis of comprehensive influence factors of rockburst, the intensity of rockburst prediction decision table was established according to the evaluation indicators, including burial depth of the rock sample,rocks' uniaxial compressive strength, the ratio of the uniaxial compressive strength to the uniaxial tensile strength of rock, the ratio of the maximum tangential stresses on cavern boundaries to the uniaxial compressive strength of rock and the elastic energy index of rock. By the attribute reduction of rough set theory(RS), the main factors of rockburst under specific geological conditions were determined, and redundant data were removed. Using particle swarm optimization (PSO) to optimize parameters of support vector machine (SVM), the main control factors of rock burst were mapped to high-dimensional space through kernel function, and the nonlinear relationship between main control factors and intensity of rockburst was fitted. Finally, a rockburst prediction model based on set theory (RS), particle swarm optimization (PSO) and support vector machine (SVM) was established. The model was applied in Daxiangling tunnel. The results show that this model has high accuracy and stability. The predict results agree well with the actual results.关键词
岩爆预测/支持向量机/粒子群算法/粗糙集理论Key words
rockburst prediction/support vector machine/particle swarm optimization algorithm/rough set theory分类
建筑与水利引用本文复制引用
李宁,王李管,贾明涛..基于粗糙集理论和支持向量机的岩爆预测[J].中南大学学报（自然科学版）,2017,48(5):1268-1275,8.基金项目
国家高技术研究发展计划(863计划)项目(2011AA060407) （863计划）
中央高校基本科研业务费专项资金资助项目(2017IVA045) (Project(2011AA060407) supported by the National Science and Technology Research and Development Program (863 Program) of China （2017IVA045）
Project(2017IVA045) supported by the Fundamental Research Funds for the Central Universities) （2017IVA045）
