摘要
为充分利用监测信息,获取围岩冻胀状态,对隧道进行冻胀安全评价,提出基于贝叶斯网络的寒区隧道冻胀信息更新和安全评价方法。该方法通过融合衬砌水平倾角变化量监测信息获取隧道冻胀状态并进行安全评价,首先根据整体冻融圈理论建立冻胀计算有限元模型,并采用截面承载力安全系数K进行安全评价;然后采用Morris敏感性分析方法对模型参数进行敏感性分析,基于筛选后的参数建立贝叶斯网络模型;最后融合监测数据获取更新后的冻胀信息并进行安全评价。研究结果表明,围岩冻胀率和冻结深度为模型的关键参数,融合监测信息后,围岩冻胀率均值由0.8%更新为0.88%;冻结深度均值由2.5 m更新为4.3m;安全系数K均值由12更新为4.9。
To effectively utilize monitoring data for asessing frost heaving information and evaluating tunnel safety in cold regions,this study proposes a Bayesian network based method for updating frost heave data and conducting safety evaluations.The method integrates monitoring data on the horizontal inclination changes of the tunnel lining,which are readily observable,to assess frost heaving status and tunel safety.A finite element model for frost heave was developed based on the freezing-thawing cycle theory,and safety was evaluated using the cross-sectional bearing capacity coefficient,K.A Morris sensitivity analysis was conducted to identify key model parameters,which informed the construction of a Bayesian network.The updated frost heaving data and safety evaluations were then derived from monitoring information.Results indicate that the perimeter rock freezing rate and freezing depth are cnitical parameters;fusing the monitoring data,the updated results show an increase in the mean freezing rate from 0.8%to 0.88%,the freezing depth from 2.5 m to4.3 m,and a reduction in the safety coefficient K from 12 to 4.9,with a failure probabilty of approximately 9.7%.
作者
韩星
张冬梅
黄忠凯
张吾渝
HAN Xing;ZHANG Dongmei;HUANG Zhongkai;ZHANG Wuyu(Department of Geotechnical Engineering,Tongji University,Shanghai 200092;College of Civil Engineering and Water Resources,Qinghai University,Xining 810016)
出处
《现代隧道技术》
CSCD
北大核心
2024年第S01期35-44,共10页
Modern Tunnelling Technology
基金
青海省科学技术厅基础研究计划项目(2023-ZJ-926M)
关键词
寒区隧道
围岩冻胀
贝叶斯网络
数据融合
安全评价
Cold region tunnel
Rock frost heaving
Bayesian network
Data fusion
Safety evaluation
