摘要
传统边坡位移反分析常采用确定性方法,忽略了参数的非唯一性,相比之下概率反分析方法通过量化输入参数的不确定性来确定多组输入参数,这对于边坡失稳概率的预测具有重要意义。通过先验分布量化输入参数的不确定性,同时构建针对原边坡数值模型的高效率代理模型,采用贝叶斯推断并结合马尔科夫链蒙特卡罗方法对输入参数进行更新,极大降低了输入参数的不确定性。通过对一实例边坡位移进行预测,并与实际监测位移进行对比,验证了此方法的可靠性与准确性。最后通过构建高效的主动学习代理模型预测了边坡的失稳概率,预测结果可为边坡失稳的定量风险评估提供重要支撑。
The traditional back analysis of slope displacement adopts the deterministic method,which ignores the non-uniqueness of parameters.In contrast,the probabilistic back analysis method can determine multiple sets of input parameters by quantifying the uncertainty of input parameters,which is of great significance for prediction of slope instability probability.In this paper,the uncertainties of input parameters were quantified by prior distribution,and then a high efficient surrogate model for the original slope numerical model was constructed.The input parameters were updated by Bayesian inference and Markov chain-Monte Carlo method,which greatly reduced the uncertainty of the input parameters.Then,by predicting the displacement of a slope and comparing with the actual monitoring displacement,the reliability and accuracy of the proposed method were verified.Finally,the slope instability probability was predicted by constructing an efficient active learning agent model.The prediction results can provide important support for quantitative risk assessment of slope′s instability.
作者
姜广伦
孔存芝
南骁聪
王升
JIANG Guanglun;KONG Cunzhi;NAN Xiaocong;WANG Sheng(Shandong Hi-Speed Yunnan Development Co.,Ltd.,Kunming 650206,China;Shandong Hi-SpeedSichuan Industrial Development Co.,Ltd.,Chengdu610041,China;Shandong Hi-Speed Engineering Inspection Co.,Ltd.,Jinan250002,China;Key Laboratory of Shandong Hi-Speed Technology and Safety Assessment,Jinan250002,China;State Key Laboratory of Geohazard Prevention and Geoenvironment Protection,Chengdu University of Technology,Chengdu610059,China)
出处
《人民长江》
北大核心
2022年第9期155-162,共8页
Yangtze River
基金
山东省交通运输厅科技计划项目(20191338)。
关键词
土质边坡
位移预测
失稳概率
概率反分析
代理模型
soil slope
displacement prediction
instability probability
probabilistic back analysis
agent model
作者简介
姜广伦,男,高级工程师,主要从事高速公路养护运营管理工作。E-mail:1624357987@qq.com;通信作者:南骁聪,男,高级工程师,硕士,主要从事岩土与隧道工程研究。E-mail:nanxiaocong1988@126.com。
