摘要
为更真实地反映土石坝渗透破坏发展的随机与时变特性,采用干湿循环土工试验与渗透系数反演等手段,从土料参数变异性及时变性角度来表征土石坝动态渗透破坏概率。将RBF神经网络和LHS法相融合对随机抽样方法进行改进,克服了传统Monte Carlo随机抽样法计算量过大的缺陷,对比证明了该改进方法的准确性与高效性。将该改进方法应用于某土石坝渗透破坏概率动态分析,计算结果表明,该土石坝渗透破坏概率总体较高并呈现逐年增大趋势,需要进行除险加固处理。
In order to reflect the stochastic and time-varying characteristics of seepage failure for earth-rock dams more truthfully,the dynamic seepage failure probability of earth-rock dams is characterized by means of geotechnical dry-wet cycle tests and permeability coefficient inversion.The fusion of RBF neural network and LHS method is used to improve the random sampling method,and the shortcomings of the traditional Monte Carlo random sampling method with heavy calculation burden are overcome.The accuracy and efficiency of the improved method are proved by comparison.The improved method is applied to the dynamic analysis of the seepage failure probability of an earth-rock dam,and the conclusion shows that the risk of seepage failure of the earth-rock dam is high and it increases year by year,indicating the need to be reinforced.
作者
欧斌
傅蜀燕
林志祥
高胜松
OU Bin;FU Shuyan;LIN Zhixiang;GAO Shengsong(College of Water Conservancy,Yunnan Agricultural University,Kunming 650201,China;State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering,Hohai University,Nanjing 210098,China;College of Mechanical and Electrical Engineering,Yunnan Agricultural University,Kunming 650201,China)
出处
《水利水电科技进展》
CSCD
北大核心
2019年第4期64-68,共5页
Advances in Science and Technology of Water Resources
基金
水文水资源与水利工程科学国家重点实验室开放基金(2016491811,2017491811)
云南省教育厅科学研究基金(2016ZZX109)
关键词
干湿循环
时变
土料参数
土石坝
渗透破坏
dry-wet cycle
time varying
soil parameters
earth-rock dam
seepage failure
