A simulation-based software to support the real-time operational parameters selection of tunnel boring machines

Abstract

With the fact that the main operational parameters of the construction process in mechanized tunneling are currently selected based on monitoring data and engineering experience without exploiting the advantages of computer methods, the focus of this work is to develop a simulation-based real-time assistant system to support the selection of operational parameters. The choice of an appropriate set of these parameters (i.e., the face support pressure, the grouting pressure, and the advance speed) during the operation of tunnel boring machines (TBM) is determined by evaluating different tunneling-induced soil-structure interactions such as the surface settlement, the associated risks on existing structures and the tunnel lining behavior. To evaluate soil-structure behavior, an advanced process-oriented numerical simulation model based on the finite cell method is utilized. To enable the real-time prediction capability of the simulation model for a practical application during the advancement of TBMs, surrogate models based on the Proper Orthogonal Decomposition and Radial Basis Functions (POD-RBF) are adopted. The proposed approach is demonstrated through several synthetic numerical examples inspired by the data of real tunnel projects. The developed methods are integrated into a user-friendly application called SMART to serve as a support platform for tunnel engineers at construction sites. Corresponding to each user adjustment of the input parameters, i.e., each TBM driving scenario, approximately two million outputs of soil-structure interactions are quickly predicted and visualized in seconds, which can provide the site engineers with a rough estimation of the impacts of the chosen scenario on structural responses of the tunnel and above ground structures.

Keywords

Numerical simulation / Surrogate model / Real-time prediction / Proper orthogonal decomposition / Radial basis functions / TBM operation / Smart construction

References

[1] Alsahly A., Stascheit J., & Meschke G. (2016). Advanced finite element modeling of excavation and advancement processes in mechanized tunneling. Advances in Engineering Software, 100, 198-214.

[2] Bilotta E., Paolillo A., Russo G., & Aversa S. (2017). Displacements induced by tunnelling under a historical building. Tunneling and Underground Space Technology, 61, 221-232.

[3] Boscardin M., & Cording E. (1989). Building response to excavation- induced settlement. ASCE Journal on Geotechnical Engineering, 115, 1-21.

[4] Buhmann M. (2003). Radial Basis Functions. Cam.

[5] Bui H. G., & Meschke G. (2020). A parallelization strategy for hydro- mechanically coupled mechanized tunneling simulations. Computers and Geotechnics, 120, 103378.

[6] Bui H., Cao B., Freitag S., Hackl K., & Meschke G. (2023). Surrogate modeling for interactive tunnel track design using the cut finite element method. Engineering with Computers.

[7] Bui H. G., Schillinger D., Zendaki Y., & Meschke G. (2022). A cutfem- based framework for numerical simulations of machine driven tunnels with arbitrary alignments. Computers and Geotechnics, 144, 104637.

[8] Bui-Thanh T., Damodaran M., & Willcox K. (2004). Aerodynamic data reconstruction and inverse design using proper orthogonal decomposition. The American Institute of Aeronautics and Astronautics (AIAA), 42, 1505-1516.

[9] Cao B., Freitag S., & Meschke G. (2016). A hybrid RNN-GPOD surrogate model for real-time settlement predictions in mechanised tunnelling. Advanced Modeling and Simulation in Engineering Sciences, 3(5), 1-22.

[10] Cao B., Freitag S., & Meschke G. (2018). A fuzzy surrogate modelling approach for real-time settlement predictions in mechanised tunnelling. International Journal of Reliability and Safety, 12(1/2), 187-217.

[11] Cao B., Obel M., Freitag S., Heußner L., Meschke G., & Mark P. (2022). Real-time risk assessment of tunneling-induced building damage considering polymorphic uncertainty. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 8( 1), 04021069.

[12] Cao B., Obel M., Freitag S., Mark P., & Meschke G. (2020). Artificial neural network surrogate modelling for real-time predictions and control of building damage during mechanised tunnelling. Advances in Engineering Software, 149, 102869 (14 pages).

[13] Cao B., Saadallah A., Egorov A., Freitag S., Meschke G., & Morik K. (2021). Online geological anomaly detection using machine learning in mechanized tunneling. In Barla, M., Di Donna, A., and Sterpi, D., editors, Challenges and Innovations in Geomechanics, Proceedings of the 16th International Conference of IACMAG (IACMAG 2021), volume 125 of Lecture Notes in Civil Engineering (pp. 323-330 ) , Turin. Springer Nature Switzerland AG.

[14] Cao B. T., Heußner L., Jodehl A., Obel M., Salloum Y., Freitag S.,... Thewes M. (2023). Interaction Modeling in Mechanized Tunneling, chapter Real-Time Simulation for Steering the Tunnel Construction. In Process (pp. 405-463). Switzerland, Cham: Springer Nature.

[15] Cheng P., Liu F., Xu Y., & Li Y. (2023). Regulating bulkhead pressure of epb shield machines through dem modeling and data mining. Underground Space, 8, 15-29.

[16] Do N. A., Dias D., Golpasand M.-R. B., Dang V. K., Nait-Rabah O., Pham V. V., & Dang T. T. (2022). Numerical analyses of twin stacked mechanized tunnels in soft grounds - influence of their position and construction procedure. Tunneling and Underground Space Technology, 130, 104734.

[17] Do N.-A., Dias D., Oreste P., & Djeran-Maigre I. (2014). Three- dimensional numerical simulation of a mechanized twin tunnels in soft ground. Tunnelling and Underground Space Technology, 42(40-51), 00052.

[18] Everson R., & Sivorich L. (1995). Karhunen-loeve procedure for gappy data. Journal of the Optical Society of America A: Optics, Image Science and Vision, 12( 8), 1657-1664.

[19] Fargnoli V., Boldini D., & Amorosi A. (2015). Twin tunnel excavation in coarse grained soils: Observations and numerical back-predictions under free field conditions and in presence of a surface structure. Tunneling and Underground Space Technology, 49, 454-469.

[20] Freitag S., Cao B., Ninic J., & Meschke G. (2018). Recurrent neural networks and proper orthogonal decomposition with interval data for real-time predictions of mechanised tunnelling processes. Computers and Structures, 207, 258-273.

[21] Freitag S., Cao B. T., Ninic´ J., & Meschke G. (2015). Hybrid surrogate modelling for mechanised tunnelling simulations with uncertain data. International Journal of Reliability and Safety, 9(2/3), 154-173.

[22] Gan X., Yu J., Gong X., & Zhu M. (2022). Probabilistic analysis for twin tunneling-induced longitudinal responses of existing shield tunnel. Tunneling and Underground Space Technology, 120, 104317.

[23] German Tunnelling Committee (DAUB) (2000). Recommendations for design and operation of shield machines. Technical report, Deutscher Ausschuss fu¨ r unterirdisches Bauen e. V. (DAUB).

[24] German Tunnelling Committee (DAUB) (2016). Recommendations for face support pressure calculations for shield tunnelling in soft ground. Technical report, Deutscher Ausschuss fu¨ r unterirdisches Bauen e. V. (DAUB).

[25] Gong C., Ding W., & Xie D. (2020). Twin epb tunneling-induced deformation and assessment of a historical masonry building on shanghai soft clay. Tunneling and Underground Space Technology, 98, 103300.

[26] Guo C., Guo Q., Zhang T., Li W., Zhu H., & Yan Z. (2022). Study on real-time heat release rate inversion for dynamic reconstruction and visualization of tunnel fire scenarios. Tunneling and Underground Space Technology, 122, 104333.

[27] Hardy R. (1990). Theory and applications of the multiquadric- biharmonic method: 20 years of discovery 1968-1988. Computers & Mathematics with Applications, 19(8-9), 163-208.

[28] He L., Liu Y., Bi S., Wang L., Broggi M., & Beer M. (2020). Estimation of failure probability in braced excavation using bayesian networks with integrated model updating. Underground Space, 5(4), 315-323.

[29] Kavvadas M., Litsas D., Vazaios I., & Fortsakis P. (2017). Development of a 3d finite element model for shield epb tunnelling. Tunneling and Underground Space Technology, 65, 22-34.

[30] Khaledi K., Miro S., Ko¨ nig M., & Schanz T. (2014). Robust and reliable metamodels for mechanized tunnel simulations. Computers and Geotechnics, 61, 1-12.

[31] Lai J., Zhou H., Wang K., Qiu J., Wang L., Wang J., & Feng Z. (2020). Shield-driven induced ground surface and ming dynasty city wall settlement of xia´n metro. Tunneling and Underground Space Technology, 97, 103220.

[32] Li C., Zhou J., Armaghani D. J., & Li X. (2021). Stability analysis of underground mine hard rock pillars via combination of finite difference methods, neural networks, and monte carlo simulation techniques. Underground Space, 6(4), 379-395.

[33] Lu¨ Q., Xiao, Z.-P., Ji, J., & Zheng, J. (2017). Reliability based design optimization for a rock tunnel support system with multiple failure modes using response surface method. Tunneling and Underground Space Technology, 70, 1-10.

[34] Maidl B., Herrenknecht M., Maidl U., & Wehrmeyer G. (2012). Mechanised shield tunnelling. Ernst und Sohn.

[35] Majumder D., Chakraborty S., & Chowdhury R. (2017). Probabilistic analysis of tunnels: A hybrid polynomial correlated function expansion based approach. Tunneling and Underground Space Technology, 70, 89-104.

[36] Marwan A., Gall V., Alsahly A., & Meschke G. (2021). Structural forces in segmental linings: Process-oriented tunnel advance simulations vs. conventional structural analysis. Tunneling and Underground. Space Technology, 111(103836).

[37] Meschke G. (1996). Consideration of aging of shotcrete in the context of a 3D viscoplastic material model. International Journal for Numerical Methods in Engineering, 39, 3123-3143.

[38] Meschke G., Freitag S., Ninic´ J., and Cao B. (2013). Simulations- und monitoring-basierte prozesssteuerung im maschinellen tunnelbau. In Kaliske, M. and Graf, W., editors, Ingenieurwissen und Vorschriften- werk, 17. Dresdner Baustatik-Seminar (pp. 127-149). Institut fu¨ r Statik und Dynamik der Tragwerke, TU Dresden.

[39] Miliziano S., & de Lillis A. (2019). Predicted and observed settlements induced by the mechanized tunnel excavation of metro line c near s. giovanni station in rome. Tunneling and Underground Space Technology, 86, 236-246.

[40] Namazi E., Mohamad H., & Hajihassani M. (2021). 3d behaviour of buildings due to tunnel induced ground movement. Transportation Geotechnics, 31, 100661.

[41] Ni P., & Mangalathu S. (2018). Fragility analysis of gray iron pipelines subjected to tunneling induced ground settlement. Tunneling and Underground Space Technology, 76, 133-144.

[42] Ninic´ J., Bui H. G., Koch C., & Meschke G. (2019). Computationally efficient simulation in urban mechanized tunneling based on multilevel bim models. Journal of Computing in Civil Engineering, 33(3), 04019007.

[43] Ninic´ J., Freitag S., & Meschke G. (2017). A hybrid finite element and surrogate modelling approach for simulation and monitoring sup- ported TBM steering. Tunnelling and Underground Space Technology, 63, 12-28.

[44] Ninic´ J., & Meschke G. (2015). Model update and real-time steering of tunnel boring machines using simulation-based meta models. Tunnelling and Underground Space Technology, 45, 138-152.

[45] Ninic´ J., & Meschke G. (2017). Simulation based evaluation of time- variant loadings acting on tunnel linings during mechanized tunnel construction. Engineering Structures, 135, 21-40.

[46] Ochmanski M., Modoni G., & Bzowka J. (2018). Automated numerical modelling for the control of epb technology. Tunneling and Underground Space Technology, 75, 117-128.

[47] Radermacher A., & Reese S. (2014). Model reduction in elastoplasticity: Proper orthogonal decomposition combined with adaptive sub- structuring. Computational Mechanics, 54(3), 677-687.

[48] Selby A. (1999). Tunnelling in soils - ground movements, and damage to buildings in workington, uk. Geotechnical and Geological Engineering, 17, 351-371.

[49] Smith T., Moehlis J., & Holmes P. (2005). Low-dimensional modelling of turbulence using proper orthogonal decomposition: A tutorial. Nonlinear Dynamics, 41, 275-307.

[50] Suwansawat S., & Einstein H. H. (2006). Artificial neural networks for predicting the maximum surface settlement caused by epb shield tunneling. Tunnelling and Underground Space Technology, 21(2), 133-150.

[51] Tao Y., He W., Sun H., Cai Y., & Chen J. (2022). Multi-objective optimization-based prediction of excavation-induced tunnel displacement. Underground Space, 7(5), 735-747.

[52] Wang Q., Fang H., & Shen L. (2016). Reliability analysis of tunnels using a metamodeling technique based on augmented radial basis functions. Tunneling and Underground Space Technology, 56, 45-53.

[53] Xie X., Yang Y., & Ji M. (2016). Analysis of ground surface settlement induced by the construction of a large-diameter shield-driven tunnel in shanghai, china. Tunneling and Underground Space Technology, 51, 120-132.

[54] Zendaki Y., Cao B., Alsahly A., Freitag S., and Meschke G. (2022). Simulation-based surrogate models for real-time tunnel lining behavior predictions. In International Conference on Computational Methods and Information Models in Tunneling (EURO: TUN), Bochum, Germany.

[55] Zendaki Y., & Meschke G. (2022). Adaptive mesh refinement using octree for finite cell simulation and its application for tunneling in saturated soils. PAMM, 22(S4).

[56] Zhang W., Li Y., Wu C., Li H., Goh A., & Liu H. (2022). Prediction of lining response for twin tunnels constructed in anisotropic clay using machine learning techniques. Underground Space, 7(1), 122-133.

[57] Zhang W., Zhang R., Wu C., Goh A. T., & Wang L. (2020). Assessment of basal heave stability for braced excavations in anisotropic clay using extreme gradient boosting and random forest regression. Underground Space, 7(2), 233-241.

[58] Zhao C., Lavasan A., Barciaga T., Ka¨mper C., Mark P., & Schanz T. (2017). Prediction of tunnel lining forces and deformations using analytical and numerical solutions. Tunneling and Underground Space Technology, 64, 164-176.

[59] Zhao T., Han T., Wu G., Gao Y., & Lu Y. (2021). Effects of grouting in reducing excessive tunnel lining deformation: Field experiment and numerical modelling using material point method. Tunneling and Underground Space Technology, 116, 104114.

[60] Zheng G., Fan Q., Zhang T., & Zhang Q. (2022). Numerical study of the soil-tunnel and tunnel-tunnel interactions of epbm overlapping tunnels constructed in soft ground. Tunneling and Underground Space Technology, 124, 104490.

[61] Zheng H., Mooney M., & Gutierrez M. (2023). Surrogate model for 3d ground and structural deformations in tunneling by the sequential excavation method. Computers and Geotechnics, 154, 105142.

[62] Zhu C., Wang S., Peng S., & Song Y. (2022). Surface settlement in saturated loess stratum during shield construction: Numerical modeling and sensitivity analysis. Tunneling and Underground Space Technology, 119, 104205.

[63] Zhuang D., Ma K., Tang C., Liang Z., Wang K., & Wang Z. (2019). Mechanical parameter inversion in tunnel engineering using support vector regression optimized by multi-strategy artificial fish swarm algorithm. Tunneling and Underground Space Technology, 83, 425-436.