Autopilot model for shield tunneling machines using support vector regression and its application to previously constructed tunnels

[1]  Zhong Zhou,et al.  Automatic detection method of tunnel lining multi‐defects via an enhanced You Only Look Once network , 2022, Comput. Aided Civ. Infrastructure Eng..

[2]  Kien-Trinh Thi Bui,et al.  Deformation forecasting of a hydropower dam by hybridizing a long short‐term memory deep learning network with the coronavirus optimization algorithm , 2022, Comput. Aided Civ. Infrastructure Eng..

[3]  Jaroslaw Adam Miszczak,et al.  Finicky transfer learning—A method of pruning convolutional neural networks for cracks classification on edge devices , 2021, Comput. Aided Civ. Infrastructure Eng..

[4]  Hong-wei Huang,et al.  An optimization strategy to improve the deep learning‐based recognition model of leakage in shield tunnels , 2021, Comput. Aided Civ. Infrastructure Eng..

[5]  Yong Qin,et al.  Hybrid deep learning architecture for rail surface segmentation and surface defect detection , 2021, Comput. Aided Civ. Infrastructure Eng..

[6]  Z. Nie,et al.  Dynamics‐based cross‐domain structural damage detection through deep transfer learning , 2021, Comput. Aided Civ. Infrastructure Eng..

[7]  Paul Schonfeld,et al.  A deep reinforcement learning approach to mountain railway alignment optimization , 2021, Comput. Aided Civ. Infrastructure Eng..

[8]  Ju An Park,et al.  Similarity learning to enable building searches in post‐event image data , 2021, Comput. Aided Civ. Infrastructure Eng..

[9]  H. Adeli,et al.  Detection of Epileptic Seizure Using Pretrained Deep Convolutional Neural Network and Transfer Learning , 2021, European Neurology.

[10]  João Paulo Papa,et al.  Deep learning techniques for recommender systems based on collaborative filtering , 2020, Expert Syst. J. Knowl. Eng..

[11]  Changming Hu,et al.  Prediction analysis of shield vertical attitude based on GRU , 2020, Journal of Physics: Conference Series.

[12]  H. Adeli,et al.  Machine learning (ML) for the diagnosis of autism spectrum disorder (ASD) using brain imaging , 2020, Reviews in the neurosciences.

[13]  Chul Min Yeum,et al.  Learning‐based image scale estimation using surface textures for quantitative visual inspection of regions‐of‐interest , 2020, Comput. Aided Civ. Infrastructure Eng..

[14]  Xueqin Chen,et al.  Tunnel condition assessment via cloud model‐based random forests and self‐training approach , 2020, Comput. Aided Civ. Infrastructure Eng..

[15]  Hojjat Adeli,et al.  A novel end‐to‐end deep learning scheme for classifying multi‐class motor imagery electroencephalography signals , 2019, Expert Syst. J. Knowl. Eng..

[16]  Lieyun Ding,et al.  Dynamic prediction for attitude and position in shield tunneling: A deep learning method , 2019, Automation in Construction.

[17]  Hojjat Adeli,et al.  A dynamic ensemble learning algorithm for neural networks , 2019, Neural Computing and Applications.

[18]  João Paulo Papa,et al.  FEMa: a finite element machine for fast learning , 2019, Neural Computing and Applications.

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

[20]  Hojjat Adeli,et al.  Novel Machine-Learning Model for Estimating Construction Costs Considering Economic Variables and Indexes , 2018, Journal of Construction Engineering and Management.

[21]  Longhua Ma,et al.  Attitude Correction System and Cooperative Control of Tunnel Boring Machine , 2018, Int. J. Pattern Recognit. Artif. Intell..

[22]  Hojjat Adeli,et al.  NEEWS: A novel earthquake early warning model using neural dynamic classification and neural dynamic optimization , 2017 .

[23]  Hojjat Adeli,et al.  Supervised Deep Restricted Boltzmann Machine for Estimation of Concrete , 2017 .

[24]  Mohammad Hossein Rafiei,et al.  A Novel Machine Learning Model for Estimation of Sale Prices of Real Estate Units , 2016 .

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

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

[27]  James V. Candy,et al.  Bayesian Signal Processing , 2009 .

[28]  Jun Sheng Chen,et al.  Study on inner force and dislocation of segments caused by shield machine attitude , 2008 .

[29]  Mitsutaka Sugimoto,et al.  Theoretical Model of Shield Behavior During Excavation. I: Theory , 2002 .

[30]  Ralf Herbrich,et al.  Learning Kernel Classifiers: Theory and Algorithms , 2001 .

[31]  Bernhard Schölkopf,et al.  Estimating the Support of a High-Dimensional Distribution , 2001, Neural Computation.

[32]  Kensuke Date,et al.  A STUDY OF POSTURE CHANGE PREDICTION OF MULTI-SHIELD TUNNELING MACHINE , 1999 .

[33]  S. Hochreiter,et al.  Long Short-Term Memory , 1997, Neural Computation.

[34]  Katsutoshi Fujisaki,et al.  A STUDY ON POSTURE CONTROL OF VERTICALLY DOUBLY-FACED SHIELD TUNNEL , 1996 .

[35]  Yoshua Bengio,et al.  Learning long-term dependencies with gradient descent is difficult , 1994, IEEE Trans. Neural Networks.

[36]  Hirokazu Akagi,et al.  FINITE ELEMENT ANALYSES OF THE STRESS-DEFORMATION BEHAVIOR CONSIDERING THE EXECUTION PROCEDURES DURING SHIELD WORK , 1993 .

[37]  N. Gordon,et al.  Novel approach to nonlinear/non-Gaussian Bayesian state estimation , 1993 .

[38]  Masaru Hoshiya,et al.  DIRECTION CONTROL METHOD OF SHIELD TUNNELLING MACHINES AND ITS OBSERVED BEHAVIOURS IN SOME VARIOUS GROUND CONDITIONS , 1993 .

[39]  Hiroshi Kuwahara,et al.  Application of fuzzy reasoning to the control of shield tunnelling. , 1988 .

[40]  Shohei Kato,et al.  A PSO based Approach to Assign Segments for Reducing Excavated Soil in Shield Tunneling , 2019, ICAART.

[41]  Vladimir Naumovich Vapni.  The Nature of Statistical Learning Theory , 1995 .

[42]  Yoshiyuki Shimizu,et al.  Movement of Shield Tunneling Machine of Articulate Type and Model Experiment. , 1994 .