长江科学院院报2017，Vol.34Issue(5)：68-74,7.DOI:10.11988/ckyyb.20160058
基于PSO-SVM算法的高放废物处置北山预选区岩爆预测
Rockburst Prediction of Beishan Pre-selected Area for Disposalof High-level Radioactive Waste Based on PSO-SVM
摘要
Abstract
For the safe disposal of high-level radioactive waste,China plans to establish an underground laboratory at buried depth of about 500 m in the granite rocks to carry out preliminary study on the disposal.However,as a common dynamic failure in deep rock engineering,rockburst always cause serious consequences.In the aim of guiding the selection of the underground laboratory site and the safe design and construction of the project,rockburst risks of shaft and tunnel excavation at different sites were predicted and analyzed based on support vector machine optimized by particle swarm optimization (PSO-SVM).One hundred groups of measured rockburst data as well as the geo-stress values and the mechanical parameters of rock mass of three candidate sites (Jiujing,Jijicao,and Xinchang) in Beishan pre-selected area were also taken as basis.Evaluation parameters including maximum tangential stress σθ of surrounding rock,uniaxial compressive strength σc,uniaxial tensile strengh σt,stress coefficient Ts,and brittleness coefficient B were chosen.Results show that PSO-SVM algorithm is feasible for rockburst prediction.The rockburst risk of engineering excavation in the depth of 300-600 m at Xinchang is the lowest.Therefore,selecting Xinchang as the construction site of underground laboratory for the disposal of high-level radioactive waste is the most secure.关键词
高放废物处置/PSO-SVM/岩爆预测/北山预选区/地下实验室Key words
disposal of high-level radioactive waste/PSO-SVM/rockburst prediction/Beishan pre-selected area/underground laboratory分类
建筑与水利引用本文复制引用
仝跃,陈亮,黄宏伟..基于PSO-SVM算法的高放废物处置北山预选区岩爆预测[J].长江科学院院报,2017,34(5):68-74,7.基金项目
国家国防科技工业局项目 （）
