Model update and real-time steering of tunnel boring machines using simulation-based meta models

A method for the simulation supported steering of the mechanized tunneling process in real time during construction is proposed. To enable real-time predictions of tunneling induced surface settlements, meta models trained a priori from a comprehensive process-oriented computational simulation model for mechanized tunneling for a certain project section of interest are introduced. For the generation of the meta models, Artificial Neural Networks (ANN) are employed in conjunction with Particle Swarm Optimization (PSO) for the model update according to monitoring data obtained during construction and for the optimization of machine parameters to keep surface settlements below a given tolerance. To provide a rich data base for the training of the meta model, the finite element simulation model for tunneling is integrated within an automatic data generator for setting up, running and postprocessing the numerical simulations for a prescribed range of parameters. Using the PSO-ANN for the inverse analysis, i.e. identification of model parameters according to monitoring results obtained during tunnel advance, allows the update of the model to the actual geological conditions in real time. The same ANN in conjunction with the PSO is also used for the determination of optimal steering parameters based on target values for settlements in the forthcoming excavation steps. The paper shows the performance of the proposed simulation-based model update and computational steering procedure by means of a prototype application to a straight tunnel advance in a non-homogeneous soil with two soil layers separated by an inclined boundary.

[1]  Kai Virtanen,et al.  Game-Theoretic Validation and Analysis of Air Combat Simulation Models , 2010, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[2]  Daniel Dias,et al.  Three dimensional simulation of slurry shield tunnelling , 2014 .

[3]  Gernot Beer.  Numerical Simulation in Tunnelling , 2003 .

[4]  Max D. Morris,et al.  Factorial sampling plans for preliminary computational experiments , 1991 .

[5]  T. Laursen.  Computational Contact and Impact Mechanics , 2003 .

[6]  Jack P. C. Kleijnen,et al.  A methodology for fitting and validating metamodels in simulation , 2000, Eur. J. Oper. Res..

[7]  W. Pitts,et al.  A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) , 2021, Ideas That Created the Future.

[8]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[9]  Hai-Sui Yu,et al.  CASM: a unified state parameter model for clay and sand , 1998 .

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

[11]  Janosch Stascheit,et al.  Parallelized computational modeling of pile–soil interactions in mechanized tunneling , 2013 .

[12]  Frank Rosenblatt,et al.  PRINCIPLES OF NEURODYNAMICS. PERCEPTRONS AND THE THEORY OF BRAIN MECHANISMS , 1963 .

[13]  Günther Meschke,et al.  On the influence of face pressure, grouting pressure and TBM design in soft ground tunnelling , 2006 .

[14]  Vladimir Buljak.  Inverse Analyses with Model Reduction: Proper Orthogonal Decomposition in Structural Mechanics , 2011 .

[15]  Malcolm D. Bolton,et al.  FINITE ELEMENT MODELLING OF EXCAVATION AND ADVANCEMENT PROCESSES OF A SHIELD TUNNELLING MACHINE , 1999 .

[16]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1992, Math. Control. Signals Syst..

[17]  Zhan Ping Song,et al.  The Tunnel Surrounding Rock Parameters Identification Method Based on PSO-ANN , 2011 .

[18]  Barry L. Nelson,et al.  Stochastic kriging for simulation metamodeling , 2008, 2008 Winter Simulation Conference.

[19]  Russell C. H. Cheng,et al.  Optimization by simulation metamodelling methods , 2004, Proceedings of the 2004 Winter Simulation Conference, 2004..

[20]  Sanjay B. Joshi,et al.  Metamodeling: Radial basis functions, versus polynomials , 2002, Eur. J. Oper. Res..

[21]  Xia-Ting Feng,et al.  Identification of visco-elastic models for rocks using genetic programming coupled with the modified particle swarm optimization algorithm , 2006 .

[22]  Eugenio Oñate,et al.  An Object-oriented Environment for Developing Finite Element Codes for Multi-disciplinary Applications , 2010 .

[23]  Tom Schanz,et al.  Global sensitivity analysis for subsoil parameter estimation in mechanized tunneling , 2014 .

[24]  Vladimir Buljak,et al.  Inverse Analyses with Model Reduction , 2012 .

[25]  Roman Lackner,et al.  Back analysis of model parameters in geotechnical engineering by means of soft computing , 2003 .

[26]  Felix Nagel,et al.  Process-oriented numerical simulation of shield-supported tunnelling in soft soils / . Prozessorientierte numerische Simulation schildgestützter Tunnelvortriebe in Lockerböden , 2010 .

[27]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[28]  Richard J. Finno,et al.  Selecting parameters to optimize in model calibration by inverse analysis , 2004 .

[29]  Jack P. C. Kleijnen.  Design and Analysis of Simulation Experiments , 2007 .

[30]  J. Kennedy,et al.  Matching algorithms to problems: an experimental test of the particle swarm and some genetic algorithms on the multimodal problem generator , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[31]  Andrea Saltelli,et al.  An effective screening design for sensitivity analysis of large models , 2007, Environ. Model. Softw..

[32]  S. Bernat,et al.  SOIL-STRUCTURE INTERACTION IN SHIELD TUNNELLING IN SOFT SOIL , 1998 .

[33]  Russell R. Barton,et al.  Simulation metamodels , 1998, 1998 Winter Simulation Conference. Proceedings (Cat. No.98CH36274).

[34]  G. Swoboda,et al.  Three-dimensional numerical modelling for TBM tunnelling in consolidated clay , 1999 .

[35]  Bernard Widrow,et al.  Adaptive switching circuits , 1988 .

[36]  Günther Meschke,et al.  A 3D finite element simulation model for TBM tunnelling in soft ground , 2004 .

[37]  Günther Meschke,et al.  A numerical study of the effect of soil and grout material properties and cover depth in shield tunnelling , 2006 .

[38]  Geoffrey E. Hinton,et al.  Learning representations by back-propagation errors, nature , 1986 .

[39]  Felix Nagel,et al.  An elasto‐plastic three phase model for partially saturated soil for the finite element simulation of compressed air support in tunnelling , 2010 .

[40]  Prospero C. Naval,et al.  Training Neural Networks Using Multiobjective Particle Swarm Optimization , 2006, ICNC.

[41]  Daniel J. Fonseca,et al.  Simulation metamodeling through artificial neural networks , 2003 .