| 基于遗传－广义回归神经元算法的坞石隧道三维弹塑性位移反分析研究 |
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| 引用本文： | 刘开云,乔春生,刘保国.基于遗传－广义回归神经元算法的坞石隧道三维弹塑性位移反分析研究[J].岩土力学,2009,30(6):1805-1809. |
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| 作者姓名： | 刘开云 乔春生 刘保国 |
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| 作者单位： | 北京交通大学,土建学院,北京,100044 |
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| 基金项目： | 国家自然科学基金，北京交通大学科技基金 |
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| 摘 要： | 广义回归神经元网络在逼近能力、学习速度和网络稳定性方面均优于BP神经元网络，且具有网络人为调节参数少的优点。本文将广义回归神经元网络引入坞石隧道工程的三维弹塑性位移反分析。为了在网络训练过程中快速搜索到最优的网络阈值，采用十进制遗传算法对网络阈值进行优化。在确定最优的网络结构后，采用遗传算法在每个待反演参数的搜索范围内搜索出与实测位移最接近的围岩力学与初始应力场参数组合。用反分析得来的参数进行下步开挖位移预测，预测值与实测值吻合较好，表明所提出的这种反分析方法在工程上是可行的，可以推广使用。
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| 关 键 词： | 隧道 数值计算 广义回归神经元 遗传算法 位移反分析 |
| 收稿时间： | 2008-02-25 |
Research on elastoplastic displacement back analysis method based on GA-GRNN algorithm in three-dimension of Wushi tunnel |
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| LIU Kai-yun,QIAO Chun-sheng,LIU Bao-guo.Research on elastoplastic displacement back analysis method based on GA-GRNN algorithm in three-dimension of Wushi tunnel[J].Rock and Soil Mechanics,2009,30(6):1805-1809. |
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| Authors: | LIU Kai-yun QIAO Chun-sheng LIU Bao-guo |
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| Institution: | School of Civil Engineering, Beijing Jiaotong University, Beijing 100044, China |
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| Abstract: | The generalized regression neural network (GRNN) is introduced into the elastoplastic displacement back analysis in three dimension of Wushi tunnel in virtue of its merits, such as good approximation capability, fast learning speed and excellent network stability. In order to find the optimal threshold value of GRNN model during training course, the genetic algorithm (GA) is combined with it to form the GA-GRNN algorithm. After determining the optimal nonlinear mapping between the numerical model parameters and the displacements, GA is used to search the elastoplastic model parameters which can minimize the error between the calculation and measured displacements of Wushi tunnel. The parameters from back analysis are inputted into the GRNN model to forecast displacement of the next construction step; and the results are very close to the measured displacement. Therefore, it is concluded that this back analysis method is feasible in tunnel engineering and can be used in similar engineering. |
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| Keywords: | tunnel numerical calculation generalized regression neural network genetic algorithm displacement back analysis |
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