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Calculation of surface settlements caused by EPBM tunneling using artificial neural network, SVM, and Gaussian processes

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Abstract

Increasing demand on infrastructures increases attention to shallow soft ground tunneling methods in urbanized areas. Especially in metro tunnel excavations, due to their large diameters, it is important to control the surface settlements observed before and after excavation, which may cause damage to surface structures. In order to solve this problem, earth pressure balance machines (EPBM) and slurry balance machines have been widely used throughout the world. There are numerous empirical, analytical, and numerical analysis methods that can be used to predict surface settlements. But substantially fewer approaches have been developed for artificial neural network-based prediction methods especially in EPBM tunneling. In this study, 18 different parameters have been collected by municipal authorities from field studies pertaining to EPBM operation factors, tunnel geometric properties, and ground properties. The data source has a preprocess phase for the selection of the most effective parameters for surface settlement prediction. This paper focuses on surface settlement prediction using three different methods: artificial neural network (ANN), support vector machines (SVM), and Gaussian processes (GP). The success of the study has decreased the error rate to 13, 12.8, and 9, respectively, which is relatively better than contemporary research.

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References

  • Alpaydın E (2010) Introduction to machine learning, 2nd edn. MIT Press, London

    Google Scholar 

  • Attewell PB, Yeates J, Selby AR (1986) Soil movement induced by tunneling and their effects on pipelines and structures. Chapman and Hall, New York

    Google Scholar 

  • Bishop CM (2006) Pattern recognition and machine learning. Springer, New York

    Google Scholar 

  • Cartwright KV (2007) Determining the effective or RMS voltage of various waveforms without calculus. Ph.D. Thesis, School of Sciences and Technology College of the Bahamas, Bahamas

  • Cheng MY, Tsai HC, Ko CH, Chang WT (2008) Evolutionary fuzzy neural inference system for decision making in geotechnical engineering. J Comput Civil Eng 22(4):272–280

    Article  Google Scholar 

  • Chiorboli MA, Marcheselli PP (1996) Analysis and control of subsidence due to earth pressure shield tunneling in Passante Ferroviario of Milano. In: Proceedings of International Conference on North American Tunneling96. Balkema, Rotterdam, pp 97–106

  • Clough GW, Leca E (1993) EPB shield tunneling in mixed face conditions. J Geotech Geoenviron 119(10):1640–1656

    Google Scholar 

  • Ding L, Ma L, Luo H, Yu M, Wu X (2011) Wavelet Analysis for tunneling-induced ground settlement based on a stochastic model. Tunn Undergr Space Technol 26(5):619–628

    Article  Google Scholar 

  • Ercelebi S, Copur H, Ocak I (2011) Surface settlement predictions for Istanbul Metro Tunnels excavated by EPB-TBM. Environ Earth Sci 62(2):357–365

    Article  Google Scholar 

  • Finno RJ, Clough GW (1985) Evaluation of soil response to EPB shield tunneling. J Geotech Geoenviron 111(2):155–173

    Google Scholar 

  • Gunn SR (1998) Support vector machines for classification and regression, University of Southamtpon, Technical Report

  • Hecht NR (1987) Kolmogorov’s mapping neural network existence theorem. In: Proceedings of the first international conference on neural networks. San Diego, USA, pp 11–14

  • Jancsecz S, Steiner W (1994) Face support for a large Mix-Shield in heterogeneous ground conditions. Tunneling, London

    Google Scholar 

  • Leca E (1989) Analysis of NATM and shield tunneling in soft ground, Ph.D. Thesis, Virginia Institute and State University, Blacksburg, VA

  • Mackay DJC (1997) Gaussian processes: a replacement for supervised neural networks, Lecture Notes

  • Matsushita Y, Hashimoto T, Iwasaki Y, Imanishi H (1995) Behavior of subway tunnel driven by large slurry shield. In: Proceedings of International Conference on Underground Construction in Soft Ground. Balkema, Rotterdam, pp 253–256

  • Mitchell TM (1997) Machine learning. McGraw Hill, New York

    Google Scholar 

  • Neal RM (1996) Bayesian learning for neural networks. Springer, New York

    Book  Google Scholar 

  • Neaupane KM, Adhikari NR (2006) Prediction of tunneling-induced ground movement with the multi-layer perceptron. Tunn Undergr Sp Tech 21(2):151–159

    Article  Google Scholar 

  • Nellessen P (2007) Using neurofuzzy systems to predict settlements for slurry shield drives based on an evaluation of the process data synchronous to the advance. In: EURO:TUN (2007) Thematic conference on computational methods in tunnelling. Austria, Vienna

    Google Scholar 

  • Ocak I (2008a) Estimating the modulus of elasticity of the rock material from compressive strength and unit weight. J S Afr Inst Min Metall 108(10):621–626

    Google Scholar 

  • Ocak I (2008b) Control of surface settlements with umbrella arch method in second stage excavations of Istanbul Metro. Tunn Undergr Sp Tech 23(6):674–681

    Article  Google Scholar 

  • Ocak I (2008c) Comparison of machine utilization time and performance for road header and impact hammer in Kadikoy–Kartal metro tunnels (Istanbul). In: 8th international scientific conference, modern management of mine producing, geology and environmental protection, vol 1. Varna, Bulgaria, pp 269–276

  • Ocak I (2009a) Empirical estimation of intact rock elastic modulus, The 21st International mining congress of Turkey. Antalya, Turkey, pp 165–172

    Google Scholar 

  • Ocak I (2009b) Environmental problems caused by Istanbul subway excavation and suggestions for remediation, environmental geology. Environ Geol 58(7):1557–1566

    Article  Google Scholar 

  • Ocak I (2009c) Environmental effects of tunnel excavation in soft and shallow ground with EPBM: the case of Istanbul. Environ Earth Sci 59(2):347–352

    Article  Google Scholar 

  • Ocak I (2011) Overview to ongoing metro projects in Istanbul, Turkey. In: 22nd world mining congress and expo. Istanbul, Turkey, pp 161–168

  • Ocak I (2012a) Interaction of deformation for twin tunnels in shallow and soft grounds excavated by EPBM. Istanbul University 24410 numbered scientific research project

  • Ocak I (2012b) Interaction of longitudinal surface settlement profile in soft metro tunneling with EPBM. ITA-AITAS World Tunnel Congress and General Assembly, Thailand

    Google Scholar 

  • Ocak I (2012c) Discussion on article ‘‘Surface subsidence induced by twin subway tunneling in soft ground conditions in Istanbul’’ by Yılmaz Mahmutoglu, Bulletin of Engineering Geology and the Environment, 2011, 70(1), pp 115–131. B Eng Geol Environ 71(2):399–400

    Article  Google Scholar 

  • Ocak I, Bilgin N (2010) Comparative studies on the performance of a road header, impact hammer and drilling and blasting method in the excavation of metro station tunnels in Istanbul. Tunn Undergr Sp Tech 25(2):181–187

    Article  Google Scholar 

  • Ocak I, Seker SE (2012) Estimation of elastic modulus of intact rocks by artificial neural network. Rock Mech Rock Eng 45(6):1047–1054

    Article  Google Scholar 

  • Ovidio J, Santos JR, Tarcisio BC (2008) Artificial neural networks analysis of Sao Paulo subway tunnel settlement data. Tunn Undergr Sp Tech 23:481–491

    Article  Google Scholar 

  • Press WH, Teukolsky SA, Vetterling WT, Flannery BP (2007) Numerical recipes: the art of scientific computing. Cambridge University Press, New York

    Google Scholar 

  • Qureshi SA, Mirza SM, Arif M (2006) Fitness function evaluation for image reconstruction using binary genetic algorithm for parallel ray transmission tomography, emerging technologies. In: ICET’06 international conference. Islamabad, Pakistan, pp 196–201

  • Rasmussen CE, Williams CKI (2007) Gaussian processes for machine learning. The MIT Press, Cambridge

    Google Scholar 

  • Rigol-Snachez JP, Chica-Olmo M, Abarca-Hernanderz F (2003) Artificial neural networks as a tool for mineral potential mapping with GIS. Int J Remote Sens 24(5):1151–1156

    Article  Google Scholar 

  • Rohmer J, Foerster E (2011) Global sensitivity analysis of large-scale numerical landslide models based on Gaussian-Process meta-modeling. Comput Geosci 37(7):917–927

    Article  Google Scholar 

  • Rosenblatt FX (1962) Principles of neurodynamics: perceptrons and the theory of brain mechanisms. Spartan Books, Washington

    Google Scholar 

  • Sakar CO, Kursun O (2010) Telediagnosis of Parkinson’s disease using measurements of dysphonia. J Med Syst 34(4):591–599

    Article  Google Scholar 

  • Schalkoff RJ (1997) Artificial neural network. McGraw Hill, New York

    Google Scholar 

  • Seyfi M, Muhaidat S, Liang J (2012) Amplify-and-forward selection cooperation over Rayleigh fading channels with imperfect CSI. IEEE T Wirel Commun 11(1):199–209

    Article  Google Scholar 

  • Shi J, Ortigao JAR, Bai JJ (1998) Modular neural networks for predicting settlements during tunneling. J Getech Geoenviron Eng 124(05):389–395

    Article  Google Scholar 

  • Stein ML (1999) Statistical interpolation of spatial data: some theory for Kriging. Springer, Chicago

    Book  Google Scholar 

  • Suwansawat S (2002) Earth pressure balance (EPB) shield tunneling in Bangkok: Ground Response and Prediction of Surface Settlements Using Artificial Neural Networks. Ph.D. Thesis, Massachusetts Institute of Technology, Cambridge, MA

  • Suwansawat S, Einstein HH (2006) Artificial neural networks for predicting the maximum surface settlement caused by EPB shield tunneling. Tunn Undergr Sp Technol 21(2):133–150

    Article  Google Scholar 

  • Tipping ME (2001) Sparse bayesian learning and the relevance vector machine. J Mach Learn Res 1:211–244

    Google Scholar 

  • Wang DD, Qiu GQ, Xie WB, Wang Y (2012) Deformation prediction model of surrounding rock based on GA-LSSVM-markov. Nat Sci 4(2):85–90

    Google Scholar 

  • Wasserman PD, Schwartz T (1988) Neural networks II. What are they and why is everybody so interested in them now? IEEE Expert 3(1):10–15

    Article  Google Scholar 

  • Xu J, Xu Y (2011) Grey correlation-hierarchical analysis for metro-caused settlement. Environ Earth Sci 64(5):1246–1256

    Article  Google Scholar 

  • Yang L, Zifeng W, Jun W, Richard AF, Robert KN, Ellsworth JW (2011) The effect of aerosol vertical profiles on satellite-estimated surface particle sulfate concentrations. Remote Sens Environ 115(2):508–513

    Article  Google Scholar 

  • Yao BZ, Yang CY, Yao JB, Sun J (2010) Tunnel surrounding rock displacement prediction using support vector machine. Int J Comput Int Sys 3(6):843–852

    Article  Google Scholar 

Download references

Acknowledgments

This study was supported by Scientific Research Projects Coordination Unit of Istanbul University. Project numbers are 24410 and YADOP-16728. The authors are grateful to Istanbul Metropolitan Municipality for supplying the data.

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Authors and Affiliations

  1. Mining Engineering Department, Engineering Faculty, Istanbul University, Avcilar, Istanbul, Turkey

    Ibrahim Ocak

  2. Computer Engineering Department, Engineering Faculty, Istanbul University, Avcilar, Istanbul, Turkey

    Sadi Evren Seker

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  1. Ibrahim Ocak
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  2. Sadi Evren Seker
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Correspondence to Ibrahim Ocak.

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Ocak, I., Seker, S.E. Calculation of surface settlements caused by EPBM tunneling using artificial neural network, SVM, and Gaussian processes. Environ Earth Sci 70, 1263–1276 (2013). https://doi.org/10.1007/s12665-012-2214-x

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  • DOI: https://doi.org/10.1007/s12665-012-2214-x

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