Abstract
In situations where rapid decisions are required or a large number of design alternatives is to be explored, numerical predictions of construction processes have to be performed in near real-time. For the design assessment of complex engineering problems such as mechanised tunnelling, simple numerical and analytical models are not able to reproduce all complex 3D interactions. To overcome this problem, in this paper a novel concept for on-demand design assessment for mechanized tunnelling using simulation-based meta models is proposed. This concept includes: (i) the generation of enhanced simulation-based meta models; (ii) real-time meta model-based design assessment in the design tool, and; (iii) the implementation within a unified numerical and information modelling platform called SATBIM. The capabilities of this concept are demonstrated through an example for the evaluation of tunnel alignment design and the assessment of the impact of tunnelling on existing infrastructure. Moreover, meta models are used for fast forward calculation in sensitivity analyses for the evaluation of the importance of model parameters. The concept proved its efficiency by assessing the design alternatives in real-time with the prediction error of less than 3% compared to complex numerical simulation in presented example.
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References
Potts, D., Zdravkovic, L.: Finite Element Analysis in Geotechnical Engineering: Application. Thomas Telford Ltd., London (2001)
Ninić, J., Meschke, G.: Model update and real-time steering of tunnel boring machines using simulation-based meta models. Tunn. Undergr. Space Technol. 45, 138–152 (2015)
Khaledi, K., Miro, S., König, M., Schanz, T.: Robust and reliable metamodels for mechanized tunnel simulations. Comput. Geotech. 61, 1–12 (2014)
Legatiuk, D., Theiler, M., Dragos, K., Smarsly, K.: A categorical approach towards metamodeling cyber-physical systems. In: Proceedings of the 11th International Workshop on Structural Health Monitoring (IWSHM). Stanford, 9 December 2017
Smarsly, K., Theiler, M., Dragos, K.: IFC-based modeling of cyber-physical systems in civil engineering. In: Proceedings of the 24th International Workshop on Intelligent Computing in Engineering (EG-ICE). Nottingham, 7 October 2017
Schulz1, W., Hermannsa, T., Khawlia, T.A.: Meta-modelling, visualization and emulation of multi-dimensional data for virtual production intelligence. AIP Conf. Proc. 1863 (2017). 440003
Suwansawat, S., Einstein, H.: Artificial neural networks for predicting the maximum surface settlement caused by EPB shield tunneling. Tunn. Undergr. Space Technol. 21(2), 133–150 (2006)
Kim, C.Y., Bae, G., Hong, S., Park, C., Moon, H., Shin, H.: Neural network based prediction of ground surface settlements due to tunnelling. Comput. Geotech. 28(6–7), 517–547 (2001)
Borrmann, A., Flurl, M., Jubierre, J.R., Mundani, R.-P., Rank, E.: Synchronous collaborative tunnel design based on consistency-preserving multi-scale models. Adv. Eng. Inform. 28(4), 499–517 (2014)
Koch, C., Vonthron, A., König, M.: A tunnel information modelling framework to support management, simulations and visualisations in mechanised tunnelling projects. Autom. Constr. 83, 78–90 (2017)
Meschke, G., Freitag, S., Alsahly, A., Ninic, J., Schindler, S., Koch, C.: Numerical simulation in mechanized tunneling in urban environments in the framework of a tunnel information model. Bauingenieur 89(11), 457–466 (2014)
Ninić, J., Koch, C., Stascheit, J.: An integrated platform for design and numerical analysis of shield tunnelling processes on different levels of detail. Adv. Eng. Softw. 112, 165–179 (2017)
Ninić, J., Koch, C., Tizani, W.: Parametric information modelling of mechanised tunnelling projects for multi-level decision support. In: 24th EG-ICE International Workshop on Computing in Engineering, vol. 1, pp. 228–238 (2017)
Montgomery, D.C., Peck, E.A., Vining, G.G.: Introduction to Linear Regression Analysis, 5th edn. Wiley, New York (2012)
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 323, 533–536 (1986)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (2000). https://doi.org/10.1007/978-1-4757-3264-1
Kennedy, J., Eberhart, R. C.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, Piscataway, pp. 1942–1948 (1995)
Morris, M.: Factorial sampling plans for preliminary computational experiments. Technometrics 33, 161–174 (1991)
Miro, S., Hartmann, D., Schanz, T.: Global sensitivity analysis for subsoil parameter estimation in mechanized tunneling. Comput. Geotech. 56, 80–88 (2014)
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikitlearn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Schindler, S., Hegemann, F., Koch, Ch., König, M., Mark, P.: Radar interferometry based settlement monitoring in tunnelling: Visualisation and accuracy analyses. Vis. Eng. 4, 7–23 (2016)
Stascheit, J., Ninić, J., Hegemann, F., Maidl, U., Meschke, G.: Building information modelling in mechanised shield tunnelling – a practitioner’s outlook to the near future. Geomech. Tunn. 11(1), 34–49 (2018)
Bui, H.G., Alsahly, A., Ninic, J., Meschke, G.: BIM-based model generation and high performance simulation of soil-structure interaction in mechanized tunnelling. In: The Fifth International Conference on Parallel, Distributed, Grid and Cloud Computing for Engineering (PARENG 2017), Paper 44. Civil-Comp Press, Stirlingshire (2017). https://doi.org/10.4203/ccp.111.44
Acknowledgements
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 702874 “SATBIM—Simulations for multi-level Analysis of interactions in Tunnelling based on the Building Information Modelling technology”. This support is gratefully acknowledged.
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Ninic, J., Koch, C., Tizani, W. (2018). Meta Models for Real-Time Design Assessment Within an Integrated Information and Numerical Modelling Framework. In: Smith, I., Domer, B. (eds) Advanced Computing Strategies for Engineering. EG-ICE 2018. Lecture Notes in Computer Science(), vol 10863. Springer, Cham. https://doi.org/10.1007/978-3-319-91635-4_11
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DOI: https://doi.org/10.1007/978-3-319-91635-4_11
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