基于有限元和神经网络的隧道结构可靠度研究
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摘要
随着我国公路建设的大规模展开,西南尤其是重庆等多山地区的隧道工程建设对设计、施工的要求越来越高。由于隧道工程的复杂性和不确定性,原有的隧道结构设计和评价方法已经不能满足需要,故运用可行的分析方法对隧道支护结构可靠性进行评价和判断是十分必要的。
本文结合国家自然科学基金重点项目“隧道及地下空间工程结构物的稳定性与可靠性”(50334060),从隧道及地下工程的模糊性和不确定性谈起,在综合评价众多结构(构件)可靠度计算方法及其在地下工程结构可靠性评判中应用的局限性和岩体及力学参数概率分布特征的基础上,以渝湘高速公路水江至界石段石龙隧道为工程背景,对施工过程中隧道围岩及初期支护结构力学特征进行了数值模拟,运用基于神经网络的蒙特卡罗有限元法对初期支护结构的可靠性进行了分析研究。
本文的主要研究内容和结论如下:
(1)系统论述了可靠指标计算的主要方法并分析了其在隧道结构等复杂系统可靠性评价中应用的局限性和优缺点。综述了神经网络基本原理及其在岩土工程中的应用,将自行编制的BP神经网络程序应用于隧道结构可靠性研究中,实现了神经网络、蒙特卡罗法及有限元方法的有机结合。
(2)结合石龙隧道勘测与试验得出的力学参数及其概率分布特征,选定随机变量E 1,μ,γ, C 1,φ1 , E 2作为输入,进行正交试验设计,运用大型通用有限元程序ANSYS对隧道浅埋段施工力学状态进行了模拟分析,获得隧道施工中围岩及支护结构的位移和力学特征。
(3)以有限元计算结果作为BP神经网络学习样本,进行网络学习训练,建立正确的网络连接。运用蒙特卡罗方法对随机变量进行了10000次伪随机抽样作为预测样本输入,以网络预测代替有限元模拟输出响应量N。
(4)运用基于神经网络的蒙特卡罗有限元法对石龙隧道浅埋段初期支护结构进行了可靠度计算,分析了不同概率分布特征的随机变量对可靠指标的影响。结果表明拱脚位置处单元可靠指标最小,但初期支护结构不可能发生压裂破坏。初期支护结构可靠指标β在随机变量服从对数正态分布时较服从正态分布时稍小。
With the large scale development of highway construction in China, the design standard and tunnel construction in Southwest China especially in Chongqing is becoming higher. Owing to the complexity and uncertainty of tunnel, the current design method and evaluation of tunnel construction could not meet the need. It’s necessary to evaluate and predict the reliability of tunnel support structure with a feasible method.
Referring to research on the stability and reliability of tunnel and underground space structure(50334060), a key project of National Nature Science Foundation of China, proceeding from the uncertainty and fuzziness of tunnel and underground engineering, and also based on the comprehensive evaluation of varieties of structures reliability calculation methods and their limitations in the applying reliability of tunnel support structure evaluation and the probability distribution features of rock mass mechanical parameter, this paper used Shilong tunnel between Shuijiang and Jieshi in Yu-Xiang Highway as engineering background and simulated the surrounding rock and supports mechanical features during construction. Applying Monte-Carlo finite element method based on neural network, this paper analyzed the reliability of initial rock mass and support structure.
The main conclusions of the paper are showed as following:
(1)The main methods of reliability index calculation and the merits were discussed systematically. Shortcomings and limitations of these methods in tunnel structure reliability were analyzed. The paper expounded the principle of neural networks and its application in rock engineering synthetically. The BP neural networks program written by ourselves was used in tunnel structure reliability analysis. Neural networks, Monte-Carlo method and finite element method were integrated commendably.
(2) Combined with the mechanical parameters and probability distribution features gained from Shilong Tunnel exploration survey and test., this paper selected the following random variables E 1,μ,γ, C 1,φ1 and E 2 as input. An obliquitous experiment was carried out. The giant general finite program-ANSYS was used to simulate and analyze the mechanical behavior of tunnel shallow buried section to gain the mechanical and displacement features of rock mass and initial support structure.
(3) This paper applied finite element program results as training sample for BP neural networks, net training practice and building correct net link. Using Monte-Carlo
本文结合国家自然科学基金重点项目“隧道及地下空间工程结构物的稳定性与可靠性”(50334060),从隧道及地下工程的模糊性和不确定性谈起,在综合评价众多结构(构件)可靠度计算方法及其在地下工程结构可靠性评判中应用的局限性和岩体及力学参数概率分布特征的基础上,以渝湘高速公路水江至界石段石龙隧道为工程背景,对施工过程中隧道围岩及初期支护结构力学特征进行了数值模拟,运用基于神经网络的蒙特卡罗有限元法对初期支护结构的可靠性进行了分析研究。
本文的主要研究内容和结论如下:
(1)系统论述了可靠指标计算的主要方法并分析了其在隧道结构等复杂系统可靠性评价中应用的局限性和优缺点。综述了神经网络基本原理及其在岩土工程中的应用,将自行编制的BP神经网络程序应用于隧道结构可靠性研究中,实现了神经网络、蒙特卡罗法及有限元方法的有机结合。
(2)结合石龙隧道勘测与试验得出的力学参数及其概率分布特征,选定随机变量E 1,μ,γ, C 1,φ1 , E 2作为输入,进行正交试验设计,运用大型通用有限元程序ANSYS对隧道浅埋段施工力学状态进行了模拟分析,获得隧道施工中围岩及支护结构的位移和力学特征。
(3)以有限元计算结果作为BP神经网络学习样本,进行网络学习训练,建立正确的网络连接。运用蒙特卡罗方法对随机变量进行了10000次伪随机抽样作为预测样本输入,以网络预测代替有限元模拟输出响应量N。
(4)运用基于神经网络的蒙特卡罗有限元法对石龙隧道浅埋段初期支护结构进行了可靠度计算,分析了不同概率分布特征的随机变量对可靠指标的影响。结果表明拱脚位置处单元可靠指标最小,但初期支护结构不可能发生压裂破坏。初期支护结构可靠指标β在随机变量服从对数正态分布时较服从正态分布时稍小。
With the large scale development of highway construction in China, the design standard and tunnel construction in Southwest China especially in Chongqing is becoming higher. Owing to the complexity and uncertainty of tunnel, the current design method and evaluation of tunnel construction could not meet the need. It’s necessary to evaluate and predict the reliability of tunnel support structure with a feasible method.
Referring to research on the stability and reliability of tunnel and underground space structure(50334060), a key project of National Nature Science Foundation of China, proceeding from the uncertainty and fuzziness of tunnel and underground engineering, and also based on the comprehensive evaluation of varieties of structures reliability calculation methods and their limitations in the applying reliability of tunnel support structure evaluation and the probability distribution features of rock mass mechanical parameter, this paper used Shilong tunnel between Shuijiang and Jieshi in Yu-Xiang Highway as engineering background and simulated the surrounding rock and supports mechanical features during construction. Applying Monte-Carlo finite element method based on neural network, this paper analyzed the reliability of initial rock mass and support structure.
The main conclusions of the paper are showed as following:
(1)The main methods of reliability index calculation and the merits were discussed systematically. Shortcomings and limitations of these methods in tunnel structure reliability were analyzed. The paper expounded the principle of neural networks and its application in rock engineering synthetically. The BP neural networks program written by ourselves was used in tunnel structure reliability analysis. Neural networks, Monte-Carlo method and finite element method were integrated commendably.
(2) Combined with the mechanical parameters and probability distribution features gained from Shilong Tunnel exploration survey and test., this paper selected the following random variables E 1,μ,γ, C 1,φ1 and E 2 as input. An obliquitous experiment was carried out. The giant general finite program-ANSYS was used to simulate and analyze the mechanical behavior of tunnel shallow buried section to gain the mechanical and displacement features of rock mass and initial support structure.
(3) This paper applied finite element program results as training sample for BP neural networks, net training practice and building correct net link. Using Monte-Carlo
引文
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[7] 赵旭峰,严松宏.响应面法在隧道衬砌结构可靠度分析中的应用[J].岩石力学与工程学报. 2003.Vol.22,No.Supp.2:2853～2856
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