| 基于改进BP网络算法的隧洞围岩分类 |
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| 引用本文： | 周翠英,张亮,黄显艺.基于改进BP网络算法的隧洞围岩分类[J].地球科学,2005,30(4):480-486. |
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| 作者姓名： | 周翠英 张亮 黄显艺 |
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| 作者单位： | [1]中山大学应用力学与工程系，广东广州510275／／中山大学地下工程与信息技术研究中心，广东广州510275／／中山大学规划设计研究院岩土工程研究所，广东广州510275 [2]中山大学地下工程与信息技术研究中心，广东广州510275／／中山大学地球科学系．广东广州510275 |
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| 摘 要： | 围岩分类对指导地下工程的设计和施工具有非常重要的意义．引入人工神经网络的方法，进行隧洞围岩分类，在传统BP算法的基础上，通过改进学习算法、优化传递函数和网络结构进行神经网络方法优化．采用附加动量法和学习速率自适应调整的策略改进学习算法，使得当误差大于上临界值时，则降低学习率，当误差小于下临界值时，则适当提高学习率，这样可加快网络的训练速度，确保网络的稳定性；通过引入调整学习率参数，使得传递过程更加敏感，加快了传递函数的收敛速度，提高了训练函数的计算精度；通过给定隐含层节点模型的取值范围，对网络结构进行优化，提高了泛化精度．将改进的BP网络模型应用于广东省东深供水改造工程的隧洞围岩分类中，分类结果与根据《水工隧洞设计规范(SL279—2002)》的分类结果完全一致，表明该方法具有良好的工程实用性．
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| 关 键 词： | 围岩分类 人工神经网络 改进BP网络算法 工程应用 |
| 文章编号： | 1000-2383(2005)04-0480-07 |
Classification of Rocks Surrounding Tunnel Based on Improved BP Network Algorithm |
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| ZHOU Cui-ying,ZHANG Liang,HUANG Xian-yi.Classification of Rocks Surrounding Tunnel Based on Improved BP Network Algorithm[J].Earth Science-Journal of China University of Geosciences,2005,30(4):480-486. |
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| Authors: | ZHOU Cui-ying ZHANG Liang HUANG Xian-yi |
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| Abstract: | The classification of rocks surrounding a tunnel has an important significance for guiding design and construction in underground engineering. This paper introduces an artificial neural network method into the classification of these rocks. Based on traditional back propagation (BP) arithmetic, an enhanced neural network method is obtained by improving the training algorithm, transfer function and network structure. By combining the additive momentum method with the self-adjusting learning speed method, the algorithm has been improved: when the error is bigger than the upper critical limits the learning speed automatically decreases; when the error is smaller than the lower critical limits the learning speed automatically increases. Thus, the training speed can be fast yet at the same time the stability of the network can be ensured. By introducing the parameter of adjusting learning speed, the transfer process becomes more sensitive and the convergent speed becomes faster, thus, increasing the calculating precision of the training function. By giving a data range for a certain implicit layer joint model, the structure of the network is optimized; correspondingly, the functional precision is improved. The improved BP network model is tested in example classifications of some typical rocks surrounding tunnels in the Dong Shen Water Supply Reconstruction Project. The results fit well with the classification according to the code of hydraulic tunnel design in China, which indicates that this improved method has a high practical application. |
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| Keywords: | classification of rocks surrounding a tunnel artificial neural network improved back propagation (BP) network algorithm engineering application |
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