白水隧道围岩力学参数敏感性分析与智能反演
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摘要
以石黔高速公路白水隧道为例,采用敏感性分析与数值模拟相结合的方法计算出隧道围岩各力学参数的敏感度,确定影响隧道变形的主要力学参数。首先,基于正交试验设计构造BP神经网络的样本,建立待反演参数与围岩变形的非线性映射关系,将现场监控量测实测值作为输入样本,得到围岩力学参数。然后,将反演得到的力学参数代入到数值模拟中,计算出隧道围岩变形值,对比分析实测值与计算值。结果表明,两者误差较小,这说明该方法应用于该工程的可行性。
Taking Baishui Tunnel of Shiqian Expressway as an example, sensitivity analysis and numerical simulation are used to calculate the sensitivity of mechanical parameters of tunnel surrounding rock and determine the main influencing parameters. Based on orthogonal experimental design, BP neural network samples are constructed to obtain the nonlinear mapping relationship between parameters to be inverted and surrounding rock deformation. The mechanical parameters of surrounding rock can be obtained by using the monitoring data of tunnel site as input samples. The inverted mechanical parameters are substituted into the numerical simulation to calculate the deformation value of surrounding rock of tunnel, and that the error between the measured and calculated values is small verifies the feasibility of the method applied to the project.
Taking Baishui Tunnel of Shiqian Expressway as an example, sensitivity analysis and numerical simulation are used to calculate the sensitivity of mechanical parameters of tunnel surrounding rock and determine the main influencing parameters. Based on orthogonal experimental design, BP neural network samples are constructed to obtain the nonlinear mapping relationship between parameters to be inverted and surrounding rock deformation. The mechanical parameters of surrounding rock can be obtained by using the monitoring data of tunnel site as input samples. The inverted mechanical parameters are substituted into the numerical simulation to calculate the deformation value of surrounding rock of tunnel, and that the error between the measured and calculated values is small verifies the feasibility of the method applied to the project.
引文
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[2]Qin Ruidong,Pan Heping,Zhao Peiqiang,et al.Petrophysical parameters prediction and uncertainty analysis in tight sandstone reservoirs using Bayesian inversion method[J].Journal of Natural Gas Science and Engineering,2018,55:431-443.
[3]王军祥,董建华,陈四利.基于DEPSO混合智能算法的岩土体应力-渗流-损伤耦合模型多参数反演研究[J].应用基础与工程科学学报,2018,26(4):872-887.
[4]王哲,涂敏,李利青.小回沟煤矿基于正交试验设计的岩体力学参数反演[J].采矿与安全工程学报,2018,35(2):397-401.
[5]张阳茁,杨杰,程琳,等.基于极限学习机的隧洞岩体蠕变参数反分析方法[J].水资源与水工程学报,2017,28(3):205-210.
[6]卢远富,包腾飞,李涧鸣,等.基于IDE-OSVR-ABAQUS的岩土力学参数反演方法[J].长江科学院院报,2017,34(6):81-87.
[7]赵杰,刘历胜,王桂萱,等.大连地铁暗挖隧道变形监测及参数智能反演[J].防灾减灾工程学报,2016,36(4):640-645.
[8]杨荷,周伟,马刚,等.基于响应面法的高堆石坝瞬变-流变参数反演方法[J].岩土力学,2016,37(6):1 697-1 705.
[9]王开禾,罗先启,沈辉,等.围岩力学参数反演的GSA-BP神经网络模型及应用[J].岩土力学,2016,37(z1):631-638.
[10]张强勇,向文,杨佳.大岗山地下厂房洞室群围岩力学参数的动态反演与开挖稳定性分析研究[J].土木工程学报,2015,48(5):90-97.
[11]姜波.隧道围岩特性参数反分析与数值模拟[J].辽宁工程技术大学学报(自然科学版),2016,35(6):603-606.
[12]李少林,周伟,马刚,等.基于改进遗传交叉算子的高心墙堆石坝参数反演[J].中南大学学报(自然科学版),2016,47(8):2 730-2 737.
[13]孙斌.基于粒子群-有限差分耦合算法的隧道岩体力学参数反演[J].铁道科学与工程学报,2014,11(3):135-139.
[14]周光裕,唐金,董亚奎.多层边坡的加权位移反分析方法[J].湖南文理学院学报(自然科学版),2015,27(1):56-59.
[15]章光,朱维申.参数敏感性分析与试验方案优化[J].岩土力学,1993(1):51-58.