摘要
为安全处置高放废物,我国拟在花岗岩体中建造埋深500 m左右的地下实验室,用以开展处置前期的相关研究。而岩爆作为深部岩石工程中一种常见的动力破坏现象,多造成严重后果。为指导地下实验室场址的筛选以及工程的安全设计施工,基于粒子群优化的支持向量机(PSO-SVM)和100组岩爆实测数据,结合北山预选区旧井、芨芨槽、新场3个候选场址的地应力值和岩体力学参数,以洞壁围岩最大切向应力σ_θ、岩石单轴抗压强度σ_c、岩石单轴抗拉强度σ_t、应力指数T_s、脆性指数B作为评判参数,对不同场址处竖井和隧道开挖的岩爆风险进行预测分析。结果表明:PSO-SVM算法用于岩爆预测是可行的;在埋深300~600 m范围内新场场址处工程开挖岩爆风险最低,以新场作为我国高放废物地下实验室的建设场址是最安全的。
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 guid-ing 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 Xin- chang） 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 σθ1,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, selec- ting Xinchang as the construction site of underground laboratory for the disposal of high-level radioactive waste is the most secure.
作者
仝跃
陈亮
黄宏伟
TONG Yu CHEN Liang HUANG Hong- wei(Key Laboratory of Geotechnical and Underground Engineering of the Ministry of Education,Tongji University, Shanghai 200092, China Department of Geotechnical Engineering,Tongji University,Shanghai 200092, China Beijing Research Institute of Uranium Geology,Beijing 100029, China)
出处
《长江科学院院报》
CSCD
北大核心
2017年第5期68-74,共7页
Journal of Changjiang River Scientific Research Institute
基金
国家国防科技工业局项目
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
disposal of high-level radioactive waste
PSO-SVM
rockburst prediction
Beishan pre-selected area
un- derground laboratory
