Prediction of Axis Attitude Deviation and Deviation Correction Method Based on Data Driven During Shield Tunneling

Due to the complex shield construction characteristics and the complex effects of geological environment, it is difficult to control the direction of shield tunneling and to determine the reasonable tunneling parameters such as tunneling speed and so on. During the tunneling, shield tunneling machine may rise, shift and snake advance, which are not conducive to control tunnel axis. Aiming at the problem that it is difficult to accurately predict and correct the axis attitude deviation in the shield tunneling process, a prediction of axis attitude deviation and deviation correction method based on data driven during shield tunneling was put forward in this paper. Under certain geological conditions, the relationship between the attitude deviation of the construction axis and tunneling parameters during shield tunneling process with different tunneling mileages is established. Based on the tunneling historical data, the XGBoost prediction model is constructed, and the axis deviation variation is predicted and analyzed with the shield construction parameters. The multi-ring deviation correction parameter calculation model based on the fusion of geometric model and association rules is designed to obtain the internal correlation of the deviation amount, the number of deviation correction rings and the deviation correction parameters of each ring under the maximum deviation of different deviation correction sections, so as to realize the accurate prediction and deviation correction of shield axis deviation in the complex construction process. Under the verification of 155 ring data in a certain subway construction section, the method proposed in this paper has higher prediction accuracy, which is important for improving the safety and quality of shield tunneling. Results from the measured shield construction ring data verify the reasonability of the proposed axis attitude deviation prediction and multi-ring deviation correction method.

[1]  Ming Yue,et al.  Double closed-loop adaptive rectification control of a shield tunneling machine with hydraulic actuator dynamics subject to saturation constraint , 2016 .

[2]  Bo Li,et al.  Evaluation of ground settlement in response to shield penetration using numerical and statistical methods: a metro tunnel construction case , 2016 .

[3]  Kourosh Shahriar,et al.  A support vector regression model for predicting tunnel boring machine penetration rates , 2014 .

[4]  Jingcheng Wang,et al.  Dynamic modeling and trajectory tracking control of Tunnel Boring Machine , 2014, The 26th Chinese Control and Decision Conference (2014 CCDC).

[5]  Tianqi Chen,et al.  XGBoost: A Scalable Tree Boosting System , 2016, KDD.

[6]  Jianrong Tan,et al.  Prediction of geological conditions for a tunnel boring machine using big operational data , 2019, Automation in Construction.

[7]  Ping Hu,et al.  Dynamic coordinated control of attitude correction for the shield tunneling based on load observer , 2012 .

[8]  Youlun Xiong,et al.  Driving force planning in shield tunneling based on Markov decision processes , 2012 .

[9]  Weizhong Guo,et al.  A novel self-adaptive thrust system of shield machine under complex geological working condition , 2017 .

[10]  Shui-Long ShenS.-L. Shen,et al.  Evaluation of the effect of rolling correction of double-o-tunnel shields via one-side loading , 2010 .

[11]  Zhibin Lin,et al.  Data-driven support vector machine with optimization techniques for structural health monitoring and damage detection , 2017, KSCE Journal of Civil Engineering.

[12]  Weidong Jin,et al.  Feature selection based on sparse representation with the measures of classification error rate and complexity of boundary , 2015 .

[13]  Huayong Yang,et al.  Automatic trajectory tracking control of shield tunneling machine under complex stratum working condition , 2012 .

[14]  Satar Mahdevari,et al.  A dynamically approach based on SVM algorithm for prediction of tunnel convergence during excavation , 2013 .

[15]  Fabrice Emeriault,et al.  Modeling the relationship between ground surface settlements induced by shield tunneling and the operational and geological parameters based on the hybrid PCA/ANFIS method , 2017 .

[16]  H. Copur,et al.  Estimating torque, thrust and other design parameters of different type TBMs with some criticism to TBMs used in Turkish tunneling projects , 2014 .

[17]  S. Shen,et al.  Analytical approach for time‐dependent groundwater inflow into shield tunnel face in confined aquifer , 2018 .

[18]  Chao Zhang,et al.  Recurrent neural networks for real-time prediction of TBM operating parameters , 2019, Automation in Construction.

[19]  T. N. Huynh,et al.  Analysis on shield operational parameters to steer articulated shield , 2016 .

[20]  Dinghua Zhang,et al.  Identification of cutting force coefficients in machining process considering cutter vibration , 2018 .

[21]  Roohollah Shirani Faradonbeh,et al.  Development of GP and GEP models to estimate an environmental issue induced by blasting operation , 2018, Environmental Monitoring and Assessment.

[22]  Danial Jahed Armaghani,et al.  Development of hybrid intelligent models for predicting TBM penetration rate in hard rock condition , 2017 .

[23]  Chengjun Shao,et al.  Trajectory Tracking Control in Horizontal Direction of Open-type Hard Rock Tunnel Boring Machine Based on Adaptive Robust Control Strategy , 2018 .

[24]  Annan Zhou,et al.  Tunneling induced geohazards in mylonitic rock faults with rich groundwater: A case study in Guangzhou , 2017 .

[25]  Dries Testelmans,et al.  Feature Selection Algorithm based on Random Forest applied to Sleep Apnea Detection , 2019, 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[26]  Jun Yang,et al.  Soil-tunnel interaction modelling for shield tunnels considering shearing dislocation in longitudinal joints , 2018, Tunnelling and Underground Space Technology.

[27]  Yilan Kang,et al.  Modeling of the thrust and torque acting on shield machines during tunneling , 2014 .

[28]  Arul Arulrajah,et al.  Prediction Model of TBM Disc Cutter Wear During Tunnelling in Heterogeneous Ground , 2018, Rock Mechanics and Rock Engineering.

[29]  Xu Yang,et al.  Pose and trajectory control of shield tunneling machine in complicated stratum , 2018, Automation in Construction.

[30]  Junhong Zhao,et al.  Dynamic load prediction of tunnel boring machine (TBM) based on heterogeneous in-situ data , 2018, Automation in Construction.

[31]  In Mo Lee,et al.  An ANN to Predict Ground Condition ahead of Tunnel Face using TBM Operational Data , 2019, KSCE Journal of Civil Engineering.

[32]  J. Bosch,et al.  Kinematic behaviour of a Tunnel Boring Machine in soft soil: Theory and observations , 2015 .

[33]  Annan Zhou,et al.  Perspectives for flood risk assessment and management for mega-city metro system , 2019, Tunnelling and Underground Space Technology.