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| Identification of TBM surrounding rock based on Markov process and deep neural network |
Yi-zhe MAO( ),Guo-fang GONG*(),Xing-hai ZHOU,Fei WANG |
| State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China |
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Abstract In order to realize the real-time identification of tunnel surrounding rock, based on the Markov method and the deep neural network model, a real-time identification method of tunnel boring machine (TBM) surrounding rock was proposed, which combined the prior surrounding rock information and the excavation parameters. The Markov process method of tunnel surrounding rock classification was used to predict the distribution probability of surrounding rock along the tunnel according to the geological exploration data of the construction site. The surrounding rock distribution probability was used as the prior rock information. The prior rock information and the TBM excavation parameters were used together as the input of the neural network, and the real surrounding rock category as an output. A deep neural network was trained to achieve real-time identification of the surrounding rock in front of the TBM. Experiments on practical engineering data showed that the overall recognition rate of the surrounding rock of the designed deep neural network model was above 96%. Compared with taking the excavation parameters as input, when the priori surrounding rock information and the excavation parameters were combined as input, the recognition rate of the surrounding rock of the model was increased by more than 6%.
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Received: 10 November 2019
Published: 25 April 2021
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| Fund: 国家重点研发计划资助项目（2018YFB1702503，2017YFB1302602，2017YFB1302604） |
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Corresponding Authors:
Guo-fang GONG
E-mail: 21825058@zju.edu.cn;gfgong@zju.edu.cn
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基于马尔可夫过程和深度神经网络的TBM围岩识别
为了实现隧道围岩的实时识别，基于马尔可夫过程和深度神经网络模型，提出将先验围岩信息和掘进参数结合，作为深度神经网络输入的隧道掘进机（TBM）围岩实时识别方法. 根据施工现场地质勘探资料，用马尔可夫过程的隧道围岩分类方法预测隧道沿线的围岩分布概率；将该围岩分布概率作为先验围岩信息，结合TBM掘进参数作为神经网络输入，真实围岩类别作为输出，训练深度神经网络以实现对TBM前方围岩的实时识别. 使用工程现场数据进行对比实验，结果表明，所设计的深度神经网络模型的围岩总体识别率高于96%. 相比于仅将掘进参数作为输入，当结合先验围岩信息和掘进参数作为输入时，模型围岩识别率提高6%以上.
关键词：
隧道掘进机（TBM）,
马尔可夫过程,
深度神经网络,
围岩识别,
预测
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