数据驱动的水下盾构隧道衬砌应变行为预测研究

doi:10.13722/j.cnki.jrme.2021.1108

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Abstract Predicting the mechanical response of underwater shield tunnel is essential to maintain the long-term stability. Therefore，a multi-factor coupling model driven by SHM(structural health monitoring) data is developed on the basic of deep learning algorithm，where Autoencoder and RNN(recurrent neural network) are adopted to learn the spatial and temporal dependencies respectively，and considering the effect of external load applied on tunnel structure. As a study case，the presented model is formalized on the monitoring data obtained from the SHM system installed in Wuhan Yangtze River tunnel. A series of data experiments are conducted to discuss the reasonability and predicted capability of the presented model. Experimental results indicated structural historical behaviors especially in last 15 days，spatial correlation，temperature，and water level are the main factors affecting the future mechanical behavior. The predicted capability of model dropped with the increase of prediction time scale. Compared with some typical models，the model presented in this study expressed the best performance，whose learning rate reaches 96%，and predictive accuracy exceeds 93% in next two weeks. As a promising application，the proposed model can be used to predict structural mechanical response under extreme conditions and provide scientific guidance for structural stability evaluation.

Key words： tunnelling engineering shield tunnel structural health monitoring machine learning prediction

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	Articles by authors
	TAN Xuyan1
	CHEN Weizhong1
	DU Bowen2
	YANG Jianping1
	ZOU Tao2

Cite this article:

TAN Xuyan1,CHEN Weizhong1,DU Bowen2, et al. Prediction for strain variation of underwater shield tunnel via data-driven modeling[J]. , 2022, 41(S2): 3317-3326.

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https://rockmech.whrsm.ac.cn/EN/10.13722/j.cnki.jrme.2021.1108 OR https://rockmech.whrsm.ac.cn/EN/Y2022/V41/IS2/3317

[1]	陈湘生，徐志豪，包小华，等. 中国隧道建设面临的若干挑战与技术突破[J]. 中国公路学报，2020，33(12)：1–14.(CHEN Xiangsheng，XU Zhihao，BAO Xiaohua，et al. Challenges and technological breakthroughs in tunnel construction in China[J]. China Journal of Highway and Transport，2020，33(12)：1–14.(in Chinese))
[2]	刘正根，黄宏伟，赵永辉，等. 沉管隧道实时健康监测系统[J]. 地下空间与工程学报，2008，4(6)：1 110–1 115.(LIU Zhenggen，HUANG Hongwei，ZHAO Yonghui，et al. Immersed tube tunnel realtime health monitoring system[J]. Chinese Journal of Underground Space and Engineering，2008，4(6)：1 110–1 115.(in Chinese))
[3]	王军，张巍. 南京长江隧道管片结构健康监测系统设计与应用[J]. 地下工程与隧道，2009，(3)：5–13.(WANG Jun，ZHANG Wei. Design and application of health monitoring system for segment structure of Nanjing Yangtze River tunnel[J]. Chinese Journal of Underground Space and Engineering，2009，(3)：5–13.(in Chinese))
[4]	刘胜春，张顶力，黄俊，等. 大型盾构隧道结构健康监测系统设计研究[J]. 地下空间与工程学报，2011，7(4)：741–748.(LIU Shengchun，ZHANG Dingli，HUANG Jun，et al. Research and design on structural health monitoring system for large-scale shield tunnel[J]. Chinese Journal of Underground Space and Engineering，2011，7(4)：741–748.(in Chinese))
[5]	朱梦琦，朱合华，王昕，等. 基于集成CART算法的TBM掘进参数与围岩等级预测[J]. 岩石力学与工程学报，2020，39(9)：1 860– 1 870.(ZHU Mengqi，ZHU Hehua，WANG Xin，et al. Study on CART- based ensemble learning algorithms for predicting TBM tunneling parameters and classing surrounding rock masses[J]. Chinese Journal of Rock Mechanics and Engineering，2020，39(9)：1 860–1 870.(in Chinese))
[6]	TAN X Y，CHEN W Z，WU G J，et al. A structural health monitoring system for data analysis of segment joint opening in an underwater shield tunnel[J]. Structural Health Monitoring，2020，19(4)：1 032–1 050.
[7]	李长俊，陈卫忠，杨建平，等. 运营期水下盾构隧道管片接缝张开度变化规律[J]. 岩土力学，2018，39(10)：3 783–3 793.(LI Changjun，CHEN Weizhong，YANG Jianping，et al. Variation of segment joint opening of underwater shield tunnel in operation period[J]. Rock and Soil Mechanics，2018，39(10)：3 783–3 793.(in Chinese))
[8]	ZHANG X L，LIN Y J，SHI C L，et al. Numerical simulation on the maximum temperature and smoke back layering length in a tilted tunnel under natural ventilation[J]. Tunnelling and Underground Space Technology，2021，107：103661.
[9]	WANG L Y，CHEN W Z，TAN X Y，et al. Evaluation of mountain slope stability considering the impact of geological interfaces using discrete fractures model[J]. Journal of Mountain Science，2019，383：2 184–2 202.
[10]	MCFEST SMITH I. Risk assessment for tunneling in adverse geological conditions[C]// Proceedings of the International Conference on Tunnels and Underground Structures. Singapore：[s. n.]，2000：625–632.
[11]	GOULET J A. Bayesian dynamic linear models for structural health monitoring[J]. Structural Control and Health Monitoring，2017，24：203.
[12]	ZHU H，WANG X，CHEN X，et al. Similarity search and performance prediction of shield tunnels in operation through time series data mining[J]. Automation in Construction，2020，114：103178.
[13]	陈映江，陈友生，穆成林. 基于回归分析法的某隧道围岩变形规律研究[J]. 成都大学学报：学报自然科学版，2015，34(3)：310–313. (CHEN Yingjiang，CHEN Yousheng，MU Chenglin. Research on tunnel surrounding rock deformation law from perspective of regression analysis[J]. Journal of Chengdu University：Natural Science，2015，34(3)：310–313.(in Chinese))
[14]	谢文国，高俊强，徐东风. 时间序列在地铁隧道变形监测数据处理中的应用[J]. 交通科技与经济，2015，17(4)：114–116.(XIE Wenguo，GAO Junqiang，XU Dongfeng. Time series prediction method used in subway deformation monitoring data processing[J]. Technology and Economy in Areas of Communications，2015，17(4)：114–116.(in Chinese))
[15]	杨建平，陈卫忠，李明，等. 水下盾构隧道运营期结构健康监测及响应规律分析[J]. 岩石力学与工程学报，2021，40(5)：902–915. (YANG Jianping，CHEN Weizhong，LI Ming，et al. Structural health monitoring and response analysis of an underwater shield tunnel during operation[J]. Chinese Journal of Rock Mechanics and Engineering，2021，40(5)：902–915.(in Chinese))
[16]	ZHANG P，WU H N，CHEN R P，et al. Hybrid metaheuristic and machine learning algorithms for tunneling induced settlement prediction：A comparative study[J]. Tunnelling and Underground Space Technology，2020，99：103383.
[17]	陈卫忠，李长俊，曾灿军，等. 大型水下盾构隧道结构健康监测系统的构建与应用[J]. 岩石力学与工程学报，2018，37(1)：1–13. (CHEN Weizhong，LI Changjun，ZENG Canjun，et al. Establishment and application of structural health monitoring system for large shield tunnel[J]. Chinese Journal of Rock Mechanics and Engineering，2018，37(1)：1–13.(in Chinese))
[18]	DU B W，LI W T，TAN X Y，et al. Development of load-temporal model to predict the further mechanical behaviors of tunnel structure under various boundary conditions[J]. Tunnelling and Underground Space Technology，2021，116：104077.
[19]	JIANG K，HAN Q，DU X，et al. Structural dynamic respons reconstruction and virtual sensing using a sequence to sequence modeling with attention mechanism[J]. Automation in Construction，2021，131：103895.
[20]	PENG H，YAN J，YU Y，et al. Time series estimation based on deep Learning for structural dynamic nonlinear prediction[J]. Structures，2021，29：1 016–1 031.
[21]	CHUNG J Y，GULCEHRE C，CHO K H，et al. Empirical evaluation of gated recurrent neural networks on sequence modeling[J]. Neural and Evolutionary Computing，2014，11：14123555.
[22]	KANG F，LIU J，LU J，et al. Concrete dam deformation prediction model for health monitoring based on extreme learning machine[J]. Structural Control and Health Monitoring，2017，24(10)：e1997.
[23]	TAN X Y，SUN X X，CHEN W Z，et al. Investigation on the data augmentation using machine learning algorithms in structural health monitoring information[J]. Structural Health Monitoring，2021，20(4)：2 054–2 068.
[24]	李明，陈卫忠，杨建平. 隧道结构在线监测数据分析方法研究[J]. 岩土力学，2016，37(4)：1 208–1 216.(LI Ming，CHEN Weizhong，YANG Jianping. An analysis method for the online monitoring data of tunnel structure[J]. Rock and Soil Mechanics，2016，37(4)：1 208– 1 216.(in Chinese))
[25]	李明，陈卫忠，杨建平，等. 基于功效系数法的隧道结构健康监测系统预警研究[J]. 岩土力学，2015，36(增2)：729–736.(LI Ming，CHEN Weizhong，YANG Jianping，et al. Early warning research for tunnel structure health monitoring system based on efficacy coefficient method[J]. Rock and Soil Mechanics，2015，36(Supp.2)：729–736.(in Chinese))