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Calculation of maximum surface settlement induced by EPB shield tunnelling and introducing most effective parameter

  • Geological, Civil, Energy and Traffic Engineering
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Abstract

This study aims to predict ground surface settlement due to shallow tunneling and introduce the most affecting parameters on this phenomenon. Based on data collected from Shanghai LRT Line 2 project undertaken by TBM-EPB method, this research has considered the tunnel’s geometric, strength, and operational factors as the dependent variables. At first, multiple regression (MR) method was used to propose equations based on various parameters. The results indicated the dependency of surface settlement on many parameters so that the interactions among different parameters make it impossible to use MR method as it leads to equations of poor accuracy. As such, adaptive neuro-fuzzy inference system (ANFIS), was used to evaluate its capabilities in terms of predicting surface settlement. Among generated ANFIS models, the model with all input parameters considered produced the best prediction, so as its associated R 2 in the test phase was obtained to be 0.957. The equations and models in which operational factors were taken into consideration gave better prediction results indicating larger relative effect of such factors. For sensitivity analysis of ANFIS model, cosine amplitude method (CAM) was employed; among other dependent variables, fill factor of grouting (n) and grouting pressure (P) were identified as the most affecting parameters.

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References

  1. ATTEWELL P B, YEATES J, SELBY A R. Soil movements induced by tunnelling and their effects on pipelines and structures [M]. London: Blackies and Sons, Ltd, 1986.

    Google Scholar 

  2. PALMSTROM A, STILLE H. Ground behaviour and rock engineering tools for underground excavations [J]. Tunnelling and Underground Space Technology, 2006, 22: 363–376.

    Article  Google Scholar 

  3. NEAUPANE K M, ADHIKARI N. Prediction of tunneling-induced ground movement with the multi-layer perceptron [J]. Tunnelling and Underground Space Technology, 2006, 21(2): 151–159.

    Article  Google Scholar 

  4. PECK R B. Deep excavation sand tunneling in soft ground [C]// Proceedings of the 7th International Conference on Soil Mechanics and Foundation Engineering. Mexico City, 1969: 225–290.

    Google Scholar 

  5. SUWANSAWAT S. Using artificial neural networks for predicting surface settlements over twin tunnels [C]// International Symposium on Underground Excavation and Tunnelling. Bangkok, Thailand, 2006: 309–318.

    Google Scholar 

  6. SHI J, ORTIGAO J, BAI J. Modular neural networks for predicting settlements during tunneling [J]. Journal of Geotechnical and Geoenvironmental Engineering, 1998, 124(5): 389–395.

    Article  Google Scholar 

  7. KIM C Y, BAE G, HONG S, PARK C, MOON H, SHIN H. Neural network based prediction of ground surface settlements due to tunnelling [J]. Computers and Geotechnics, 2001, 28(6): 517–547.

    Article  Google Scholar 

  8. SUWANSAWAT S, EINSTEIN H. Artificial neural networks for predicting the maximum surface settlement caused by EPB shield tunneling [J]. Tunnelling and Underground Space Technology, 2006, 21(2): 133–150.

    Article  Google Scholar 

  9. BOUBOU R, EMERIAULT F, KASTNER R. Artificial neural network application for the prediction of ground surface movements induced by shield tunnelling [J]. Canadian Geotechnical Journal, 2010, 47(11): 1214–1233.

    Article  Google Scholar 

  10. DARABI A, AHANGARI K, NOORZAD A, ARAB A. Subsidence estimation utilizing various approaches—A case study: Tehran No. 3 subway line [J]. Tunnelling and Underground Space Technology, 2012, 31: 117–127.

    Article  Google Scholar 

  11. OCAK I, SEKER S E. Calculation of surface settlements caused by EPBM tunneling using artificial neural network, SVM, and Gaussian processes [J]. Environmental Earth Sciences, 2013, 70(3): 1263–1276.

    Article  Google Scholar 

  12. AHANGARI K, MOEINOSSADAT S R, BEHNIA D. Estimation of tunnelling-induced settlement by modern intelligent methods [J]. Soils and Foundations, 2015, 55: 737–748.

    Article  Google Scholar 

  13. HOU J, ZHANG M, TU M. Prediction of surface settlements induced by shield tunneling: An ANFIS model [M]. London: Taylor & Francis Group, 2009: 551–554.

    Google Scholar 

  14. QIAO J, LIU J, GUO W, ZHANG Y. Artificial neural network to predict the surface maximum settlement by shield tunneling [C]// ICIRA’10 Proceedings of the Third International Conference on Intelligent Robotics and Applications. 2010: 257–265.

    Chapter  Google Scholar 

  15. JONG Y H, LEE C I. Influence of geological conditions on the powder factor for tunnel blasting [J]. International Journal of Rock Mechanics and Mining Sciences, 2004, 41(1): 533–538.

    Article  Google Scholar 

  16. WHITTAKER B, REDDISH D. Subsidence: Occurrence, prediction and control (Developments in geotechnical engineering, 56) [M]. Amsterdam: Elsevier, 1989.

    Google Scholar 

  17. KHANDELWAL M, SINGH TN. Prediction of blast induced ground vibrations and frequency in opencast mine: a neural network approach [J]. Journal of Sound and Vibration, 2006, 289(4): 711–725.

    Article  Google Scholar 

  18. HAGHNEJAD A, AHANGARI K, NOORZAD A. Investigation on various relations between uniaxial compressive strength, elasticity and deformation modulus of asmari formation in Iran [J]. Arabian Journal for Science and Engineering, 2014, 39: 2677–2682.

    Article  Google Scholar 

  19. BEHNIA D, AHANGARI K, NOORZAD A, MOEINOSSADAT S R. Predicting crest settlement in concrete face rockfill dams using adaptive neuro-fuzzy inference system and gene expression programming intelligent methods [J]. Journal of Zhejiang University: Science A (Applied Physics & Engineering), 2013, 14(8): 589–602.

    Article  Google Scholar 

  20. GARG A, TAI K, VIJAYARAGHAVAN V, SINGRU P M. Mathematical modelling of burr height of the drilling process using a statistical-based multi-gene genetic programming approach [J]. The International Journal of Advanced Manufacturing Technology, 2014, 73: 113–126.

    Article  Google Scholar 

  21. JANG J S R. ANFIS: Adaptive-network-based fuzzy inference systems [J]. IEEE Transactions on Systems, Man, and Cybernetics, 1993, 23(3): 665–685.

    Article  Google Scholar 

  22. KARTALOPOULOS S V. Understanding neural networks and fuzzy logic [M]// Basic Concepts and Applications. IEEE Press, 1996.

    Google Scholar 

  23. JANG J S R, SUN C T. Neuro-fuzzy modeling and control [J]. Proceedings IEEE, 1997, 83(3): 378–406.

    Article  Google Scholar 

  24. JANG J S R, SUN C T, MIZUTANI E. Neuro-fuzzy and soft computing a computational approach to learning and machine intelligence [M]. New Jersey: Prentice Hall, 1997.

    Google Scholar 

  25. BEHNIA D, MOEINOSSADAT S R, BEHNIA B, BEHNIA M, SAFARI-GORGI A, ZAKERIAN P. Prediction of settlement in sloping core rockfill dams using soft-computing [J]. Research in Civil and Environmental Engineering (RCEE), 2014, 2(2): 55–65.

    Google Scholar 

  26. JALALIFAR H, MOJEDIFAR S, SAHEBI A A, NEZAMABADIPOUR H. Application of the adaptive neuro-fuzzy inference system for prediction of a rock engineering classification system [J]. Computers and Geotechnics, 2011, 38: 783–790.

    Article  Google Scholar 

  27. BEZDEK J. Fuzzy mathematics in pattern classification [D]. Ithaca: Cornell University, 1973.

    Google Scholar 

  28. ROSE T J. Fuzzy logic with engineering applications [M]. 3rd ed. New Mexico: John Wiley & Sons, Ltd, 2010.

    Book  Google Scholar 

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Authors and Affiliations

  1. Department of Mining Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Sayed Rahim Moeinossadat & Kaveh Ahangari

  2. Department of Mining and Metallurgical Engineering, Amirkabir University of Technology, Tehran, Iran

    Kourosh Shahriar

Authors
  1. Sayed Rahim Moeinossadat
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  2. Kaveh Ahangari
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  3. Kourosh Shahriar
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Correspondence to Kaveh Ahangari.

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Moeinossadat, S.R., Ahangari, K. & Shahriar, K. Calculation of maximum surface settlement induced by EPB shield tunnelling and introducing most effective parameter. J. Cent. South Univ. 23, 3273–3283 (2016). https://doi.org/10.1007/s11771-016-3393-5

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  • DOI: https://doi.org/10.1007/s11771-016-3393-5

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