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| Tunnel Deformation Prediction Based on Modified Support Vector Machine |
| ZHOU Qi-cai , FAN Si-xia, ZHAO Jiong, XIONG Xiao-lei
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| Tongji University, Shanghai 201804, China |
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Abstract Abstract：Research purposes： In order to remove the noise of metro tunnel deformation monitoring data which affected by environmental factors and increase the extrapolated predictive ability of deformation data, a new prediction method based on discrete wavelets and a modified support vector machine (SVM) is proposed.
Research conclusions：(1) The noise of tunnel deformation data can be removed by discrete wavelet analysis and the low frequency effective information can be acquired. (2) A dynamic on-line sliding window technique is introduced. Additionally, the final training samples and the training samples of the tunnel deformation prediction model could be determined by utilizing the two orders of sliding windows which aiming at dynamically adjusting and updating the number of samples. This could improve data utilization rate. (3) The performance of prediction model has been validated with Shanghai metro deformation data under steady state and unsteady state. The results show that this method has good de-noising effect and high precision. Also, the prediction model could be transferred to engineering application and may give references to the construction of tunnel prediction model.
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［1］冯夏庭. 智能岩石力学导论［M］. 北京:科学出版社，2000.
Feng Xiating. Introduce of Intelligent Rock Mechanics［M］. Beijing: Science Press, 2000.
［2］李广信．高等土力学［M］．北京：清华大学出版社, 2004.
Li Guangxin. Advanced Soil Mechanics［M］. Beijing: Tsinghua University Press, 2004.
［3］韦凯. 基于蚁群算法的软土盾构隧道长期不均匀沉降预测模型［D］. 上海：同济大学，2009.
Wei Kai. Forecast of Uneven Long-term Settlement of Metro Tunnel on the Basis of Ant Colony Optimization in Soft Soil［D］. Shanghai：Tongji University,2009.
［4］Cortes C, Vapnik V. Support Vector Networks［J］. Machine Learning, 1995(3):273-297.
［5］Ding L Y, Ma L, Luo H B, etc. Wavelet Analysis for Tunneling Induced Ground Settlement Based on a Stochastic Model［J］. Tunnelling and Underground Space Technology,2011(5):619-628.
［6］Hui X, Bi W H, Hou X L, et.al. Reliability Analysis Using Radial Basis Function Networks and Support Vector Machines［J］. Computers and Geotechnics. 2011(2):178-186.
［7］Stefano M, Giuseppe J. Terminated Ramp-support Vector Machines: a Nonparametric Data Dependent Kernel［J］. Neural Networks, 2006(10):1597-1611．
［8］Liu H, Wen S, Li W, Xu C, Hu C. Study on Identification of Oil/gas and Water Zones in Geological Logging Base on Support-vector Machine［J］. Fuzzy Inform Eng,2009(2):849-857.
［9］范思遐,周奇才,熊肖磊,等. 基于粒子群与支持向量机的隧道变形预测模型［J］. 计算机工程与应用，2014(5):6-10.
Fan Sixia, Zhou Qicai, Xiong Xiaolei, etc. A Tunnel Deformation Prediction Model Based on Support Vector Machine with Particle Swarm Optimization Algorithm［J］. Computer Engineering and Application,2014(5):6-10.
［10］周奇才,范思遐, 赵炯, 等. 基于Multi-agent的隧道变形监测系统［J］. 铁道工程学报，2014(9):77-82.
Zhou Qicai, Fan Sixia, Zhao Jiong,etc. Tunnel Deformation Monitoring System Based on Multi-agent Technique［J］. Journal of Railway Engineering Society, 2014(9):77-82.
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