A multi-channel decoupled deep neural network for tunnel boring machine torque and thrust prediction

[1]  Chengliang Liu,et al.  A multi-stage data augmentation and AD-ResNet-based method for EPB utilization factor prediction , 2023, Automation in Construction.

[2]  Chengliang Liu,et al.  A novel LSTM-autoencoder and enhanced transformer-based detection method for shield machine cutterhead clogging , 2023, Science China Technological Sciences.

[3]  Dalong Jin,et al.  Face stability analysis of cohesion-frictional soils considering the soil arch effect and the instability failure process , 2023, Computers and Geotechnics.

[4]  Chengliang Liu,et al.  Geological information prediction for shield machine using an enhanced multi-head self-attention convolution neural network with two-stage feature extraction , 2022, Geoscience Frontiers.

[5]  Q. Zhang,et al.  Physics-based machine learning method and the application to energy consumption prediction in tunneling construction , 2022, Adv. Eng. Informatics.

[6]  Chengjin Qin,et al.  A gene expression programming-based method for real-time wear estimation of disc cutter on TBM cutterhead , 2022, Neural Computing and Applications.

[7]  Annan Zhou,et al.  Real-time analysis and prediction of shield cutterhead torque using optimized gated recurrent unit neural network , 2022, Journal of Rock Mechanics and Geotechnical Engineering.

[8]  Yang Liu,et al.  Numerical investigation of rock dynamic fragmentation during rockslides using a coupled 3D FEM-DEM method , 2022, Journal of Mountain Science.

[9]  X. Ling,et al.  Random forest-based predictors for driving forces of earth pressure balance (EPB) shield tunnel boring machine (TBM) , 2022, Tunnelling and Underground Space Technology.

[10]  Chengliang Liu,et al.  Concentrated velocity synchronous linear chirplet transform with application to robotic drilling chatter monitoring , 2022, Measurement.

[11]  Quansheng Liu,et al.  A real-time prediction method for tunnel boring machine cutter-head torque using bidirectional long short-term memory networks optimized by multi-algorithms , 2022, Journal of Rock Mechanics and Geotechnical Engineering.

[12]  Haiqing Yang,et al.  Automated Recognition Model of Geomechanical Information Based on Operational Data of Tunneling Boring Machines , 2022, Rock Mechanics and Rock Engineering.

[13]  Yixiang Huang,et al.  Fault Knowledge Transfer Assisted Ensemble Method for Remaining Useful Life Prediction , 2021, IEEE Transactions on Industrial Informatics.

[14]  Chengliang Liu,et al.  An adaptive hierarchical decomposition-based method for multi-step cutterhead torque forecast of shield machine , 2022, Mechanical Systems and Signal Processing.

[15]  Chengjin Qin,et al.  A novel constrained dense convolutional autoencoder and DNN-based semi-supervised method for shield machine tunnel geological formation recognition , 2022 .

[16]  Chengjin Qin,et al.  An accurate and adaptative cutterhead torque prediction method for shield tunneling machines via adaptative residual long-short term memory network , 2022 .

[17]  Qian Zhang,et al.  Prediction of Geological Parameters during Tunneling by Time Series Analysis on In Situ Data , 2021, Comput. Intell. Neurosci..

[18]  Yilan Kang,et al.  Total loads modeling and geological adaptability analysis for mixed soil-rock tunnel boring machines , 2021, Underground Space.

[19]  Rana Khallaf,et al.  Classification and analysis of deep learning applications in construction: A systematic literature review , 2021 .

[20]  Shui-Hua Jiang,et al.  Intelligent assistant driving method for tunnel boring machine based on big data , 2021, Acta Geotechnica.

[21]  Chengjin Qin,et al.  Rock mass type prediction for tunnel boring machine using a novel semi-supervised method , 2021, Measurement.

[22]  Yixiang Huang,et al.  Unsupervised machine fault diagnosis for noisy domain adaptation using marginal denoising autoencoder based on acoustic signals , 2021 .

[23]  Bin Liu,et al.  Intelligent Decision Method for Main Control Parameters of Tunnel Boring Machine based on Multi-Objective Optimization of Excavation Efficiency and Cost , 2021, ArXiv.

[24]  Chengjin Qin,et al.  A field parameters-based method for real-time wear estimation of disc cutter on TBM cutterhead , 2021 .

[25]  Yanrui Jin,et al.  Precise cutterhead torque prediction for shield tunneling machines using a novel hybrid deep neural network , 2021 .

[26]  Limao Zhang,et al.  Multi-objective optimization in tunnel line alignment under uncertainty , 2021 .

[27]  Chengliang Liu,et al.  A Novel A-CNN Method for TBM Utilization Factor Estimation , 2021 .

[28]  Liao Jianbin,et al.  A TBM advance rate prediction method considering the effects of operating factors , 2021 .

[29]  Jinhui Li,et al.  Advanced prediction of tunnel boring machine performance based on big data , 2021, Geoscience Frontiers.

[30]  Kun Zhang,et al.  Evolutionary hybrid neural network approach to predict shield tunneling-induced ground settlements , 2020 .

[31]  Reza Mikaeil,et al.  Feasibility of Intelligent Models for Prediction of Utilization Factor of TBM , 2020, Geotechnical and Geological Engineering.

[32]  Zhang Wengang,et al.  Use of soft computing techniques for tunneling optimization of tunnel boring machines , 2020 .

[33]  Zengda Guan,et al.  Improved support vector regression models for predicting rock mass parameters using tunnel boring machine driving data , 2019, Tunnelling and Underground Space Technology.

[34]  Qian Zhang,et al.  An Approach Integrating Dimensional Analysis and Field Data for Predicting the Load on Tunneling Machine , 2019, KSCE Journal of Civil Engineering.

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

[36]  Danial Jahed Armaghani,et al.  Development of a new hybrid ANN for solving a geotechnical problem related to tunnel boring machine performance , 2019, Engineering with Computers.

[37]  Xiaoping Zhou,et al.  A probabilistic method to analyze collapse failure of shallow rectangular tunnels , 2018, Tunnelling and Underground Space Technology.

[38]  Xiaoping Zhou,et al.  Reliability Assessment of Tunnel Based on P -Wave Seismic Velocity , 2018, International Journal of Geomechanics.

[39]  Wei-zhong Chen,et al.  Novel rockburst criterion based on the TBM tunnel construction of the Neelum–Jhelum (NJ) hydroelectric project in Pakistan , 2018, Tunnelling and Underground Space Technology.

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

[41]  Takehisa Yairi,et al.  A review on the application of deep learning in system health management , 2018, Mechanical Systems and Signal Processing.

[42]  H. Copur,et al.  Empirical modeling for predicting excavation performance of EPB TBM based on soil properties , 2018 .

[43]  Xibing Li,et al.  Rockburst characteristics and numerical simulation based on a strain energy density index: A case study of a roadway in Linglong gold mine, China , 2017 .

[44]  Song Jie,et al.  An overview of ahead geological prospecting in tunneling , 2017 .

[45]  Wei Sun,et al.  Multidisciplinary design optimization of tunnel boring machine considering both structure and control parameters under complex geological conditions , 2016 .

[46]  Jianping Liu,et al.  Application and development of hard rock TBM and its prospect in China , 2016 .

[47]  Jian Zhao,et al.  Challenges and opportunities of using tunnel boring machines in mining , 2016 .

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

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

[50]  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 .

[51]  Qiping Shen,et al.  Factors affecting effectiveness and efficiency of analyzing stakeholders' needs at the briefing stage of public private partnership projects , 2013 .

[52]  Bak Kong Low,et al.  Probabilistic evaluation of ground-support interaction for deep rock excavation using artificial neural network and uniform design , 2012 .

[53]  Yilan Kang,et al.  Identification and optimization of energy consumption by shield tunnel machines using a combined mechanical and regression analysis , 2012 .

[54]  T. Speed.  A Correlation for the 21st Century , 2011, Science.

[55]  Hu Shi,et al.  Determination of the cutterhead torque for EPB shield tunneling machine , 2011 .

[56]  O. Acaroglu,et al.  Prediction of thrust and torque requirements of TBMs with fuzzy logic models , 2011 .

[57]  J. Rostami,et al.  Disc cutting tests in Colorado Red Granite: Implications for TBM performance prediction , 2007 .

[58]  Bernhard Maidl,et al.  Mechanised Shield Tunnelling , 1996 .

[59]  Xueguan Song,et al.  Generative adversarial network for geological prediction based on TBM operational data , 2022 .