Abstract
The ground loss caused by earth pressure balance (EPB) shield tunneling is a serious threat to the safety of ground and underground buildings. The amount of soil discharged is a direct factor causing the ground loss, but the feedback of the amount of soil discharged has the problems of human subjectivity and information lag. Based on the theory of soil discharged and shield construction data, a data model for predicting the amount of soil discharged is constructed, and a predictive method of soil discharged based on adaptive adjustment strategy and parameters to achieve optimal regression is proposed. Taking Changzhou shield tunnel construction project as an example, through comparative analysis, the main influencing factors of soil discharge data model are screw-conveyor torque, screw-conveyor revolutions, total thrust, screw-conveyor entrance earth pressure, the revised standard penetration test blow-count, and compression modulus. The results show that the mean absolute error (MAE) of the model is 0.911 m3, and the mean absolute percentage error (MAPE) is 2.78%, which verifies the effectiveness of the prediction of soil discharged by feature engineering
Similar content being viewed by others
References
Ákos T, Gong Q, Zhao J (2013) Case studies of TBM tunnelling performance in rock-soil interface mixed ground. Tunnelling & Underground Space Technology Incorporating Trenchless Technology Research 38(9):140–150, DOI: https://doi.org/10.1016/j.tust.2013.06.001
Anagnostou G, Kovári K (1996) Face stability conditions with earth-pressure-balanced shields. Tunnelling and Underground Space Technology 11(2):165–173, DOI: https://doi.org/10.1016/0886-7798(96)00017-X
Anitescu C, Atroshchenko E, Alajlan N, Rabczuk T (2019) Artificial neural network methods for the solution of second order boundary value problems. Computers, Materials and Continua 59(1):345–359
Bouayad D, Emeriault F (2017) Modeling the relationship between ground surface settlements induced by shield tunneling and the operational and geological parameters based on the hybrid PCA/ANFIS method. Tunnelling and Underground Space Technology 68: 142–152, DOI: https://doi.org/10.1016/j.tust.2017.03.011
Brereton RG, Lloyd GR (2010) Support vector machines for classification and regression. Analyst 135(2):230–267
Brest J, Greiner S, Boskovic B, Mernik M, Zumer V (2006) Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems. IEEE Transactions on Evolutionary Computation 10(6):646–657, DOI: https://doi.org/10.1109/TEVC.2006.872133
Cattoni E, Miriano C, Boco L, Tamagnini C (2016) Time-dependent ground movements induced by shield tunneling in soft clay: A parametric study. Acta Geotechnica 11(6):1385–1399, DOI: https://doi.org/10.1007/s11440-016-0452-x
Chen RP, Lin XT, Xin K, Zhong ZQ, Liu Y, Zhang P, Wu HN (2018) Deformation and stress characteristics of existing twin tunnels induced by close-distance EPBS under-crossing. Tunnelling and Underground Space Technology 82:468–481, DOI: https://doi.org/10.1016/j.tust.2018.08.059
Cherkassky V, Ma Y (2004) Practical selection of SVM parameters and noise estimation for SVM regression. Neural Networks 17(1):113–126, DOI: https://doi.org/10.1016/S0893-6080(03)00169-2
Chung CI (1970) New ideas about solids conveying in screw extruders. SPE Journal 26(5):32–44
Darnell WH, Mol EAJ (1956) Solids conveying in extruders. SPE Journal 12(4):20–29
Dindarloo RS, Siami-Irdemoosa E (2015) Maximum surface settlement based classification of shallow tunnels in soft ground. Tunnelling and Underground Space Technology 49:320–327, DOI: https://doi.org/10.1016/j.tust.2015.04.021
Es-Haghi MS, Shishegaran A, Rabczuk T (2020) Evaluation of a novel asymmetric genetic algorithm to optimize the structural design of 3D regular and irregular steel frames. Frontiers of Structural and Civil Engineering 14(5):1110–1130, DOI: https://doi.org/10.1007/s11709-020-0643-2
Fan Q, Wang W, Yan X (2017a) Differential evolution algorithm with strategy adaptation and knowledge-based control parameters. Artificial Intelligence Review 51(2):219–253, DOI: https://doi.org/10.1007/s10462-017-9562-6
Fan Q, Yan X, Xue Y (2017b) Prior knowledge guided differential evolution. Soft Computing 21(22):6841–6858, DOI: https://doi.org/10.1007/s00500-016-2235-6
Fang Y, He C, Nazem A, Yao Z, Grasmick J (2017) Surface settlement prediction for EPB shield tunneling in sandy ground. KSCE Journal of Civil Engineering 21(11):2908–2918, DOI: https://doi.org/10.1007/s12205-017-0989-8
Fattahi H, Babanouri N (2017) Applying optimized support vector regression models for prediction of tunnel boring machine performance. Geotechnical and Geological Engineering 35(5):2205–2217, DOI: https://doi.org/10.1007/s10706-017-0238-4
Festa D, Broere W, Bosch JW (2012) An investigation into the forces acting on a TBM during driving-mining the TBM logged data. Tunnelling and Underground Space Technology 32:143–157, DOI: https://doi.org/10.1016/j.tust.2012.06.006
Guo SM, Yang CC (2015) Enhancing differential evolution utilizing eigenvector-based crossover operator. IEEE Transactions on Evolutionary Computation 19(1):31–49, DOI: https://doi.org/10.1109/TEVC.2013.2297160
Guo HW, Zhuang XY, Rabczuk T (2019) A deep collocation method for the bending analysis of Kirchhoff plate. Computers, Materials and Continua 59(2):433–456, DOI: https://doi.org/10.32604/cmc.2019.06660
Hamdia KM, Zhuang XY, Rabczuk T (2021) An efficient optimization approach for designing machine learning models based on genetic algorithm. Neural Computing and Applications 2021(33):1923–1933, DOI: https://doi.org/10.1007/s00521-020-05035-x
Islam SM, Das S, Ghosh S, Roy S, Suganthan PN (2012) An adaptive differential evolution algorithm with novel mutation and crossover strategies for global numerical optimization. IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 42(2):482–500, DOI: https://doi.org/10.1109/TSMCB.2011.2167966
Lee KM, Rowe RK, Lo KY (1992) Subsidence owing to tunnelling. I. Estimating the gap parameter. Canadian Geotechnical Journal 29(6):929–940, DOI: https://doi.org/10.1139/t92-104
Liang R, Wu W, Yu F, Jiang G, Liu J (2018) Simplified method for evaluating shield tunnel deformation due to adjacent excavation. Tunnelling and Underground Space Technology 71:94–105, DOI: https://doi.org/10.1016/j.tust.2017.08.010
Liao SM, Liu JH, Wang RL, Li ZM (2011) Shield tunneling and environment protection in shanghai soft ground. Tunnelling & Underground Space Technology Incorporating Trenchless Technology Research 24(4):454–465, DOI: https://doi.org/10.1016/j.tust.2008.12.005
Liu X, Fang Q, Zhang D, Liu Y (2020) Energy-based prediction of volume loss ratio and plastic zone dimension of shallow tunnelling. Computers and Geotechnics 118:103343, DOI: https://doi.org/10.1016/j.compgeo.2019.103343
Liu XY, Shao C, Ma HF, Liu RX (2011) Optimal earth pressure balance control for shield tunneling based on LS-SVM and PSO. Automation in Construction 20(4):321–327, DOI: https://doi.org/10.1016/j.autcon.2010.11.002
Lovegrove JGA, Williams JG (1973a) Solids conveying in a single screw extruder; A comparison of theory and experiment. Journal of Mechanical Engineering Science 15(3):195–199, DOI: https://doi.org/10.1243/JMES_JOURJ973_015_034_02
Lovegrove JGA, Williams JG (1973b) Solids conveying in a single screw extruder; The rôle of gravity forces. Journal of Mechanical Engineering Science 15(2):114–122, DOI: https://doi.org/10.1243/JMES_JOUR_1973_015_021_02
Meng FY, Chen RP, Kang X (2018) Effects of tunneling-induced soil disturbance on the post-construction settlement in structured soft soils. Tunnelling and Underground Space Technology 80:53–63, DOI: https://doi.org/10.1016/j.tust.2018.06.007
Merritt AS, Mair RJ (2006) Mechanics of tunneling machine screw conveyors: Model tests. Géotechnique (9):605–615
Merritt AS, Mair RJ (2008) Mechanics of tunnelling machine screw conveyors: A theoretical model. Géotechnique 58(2):79–94
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. Tunnelling and Underground Space Technology 86:236–246, DOI: https://doi.org/10.1016/j.tust.2019.01.022
Min C, Mao S, Liu Y (2014) Big data: A survey. Mobile Networks and Applications 19(2):171–209, DOI: https://doi.org/10.1007/s11036-013-0489-0
Moeinossadat SR, Ahangari K (2019) Estimating maximum surface settlement due to EPBM tunneling by numerical-intelligent approach — A case study: Tehran subway line 7. Transportation Geotechnics 18:92–102, DOI: https://doi.org/10.1016/j.trgeo.2018.11.009
Mortazavi B, Podryabinkin EV, Roche S, Rabczuk T, Zhuang XY, Shapeev AV (2020) Machine-learning interatomic potentials enable first-principles multiscale modeling of lattice thermal conductivity in graphene/borophene heterostructures. Materials Horizons 7: 2359–2367, DOI: https://doi.org/10.1039/D0MH00787K
Moysey PA, Thompson MR (2005) Modelling the solids inflow and solids conveying of single-screw extruders using the discrete element method. Powder Technology 153(2):95–107, DOI: https://doi.org/10.1016/j.powtec.2005.03.001
Naghsh MA, Shishegaran A, Karami B, Rabczuk T, Shishegaran A, Taghavizadeh H, Moradi M (2021) An innovative model for predicting the displacement and rotation of column-tree moment connection under fire. Frontiers of Structural and Civil Engineering 15(1):194–212, DOI: https://doi.org/10.1007/s11709-020-0688-2
Peila D (2014) Soil conditioning for EPB shield tunnelling. KSCE Journal of Civil Engineering 18(4):831–836, DOI: https://doi.org/10.1007/s12205-014-0023-3
Qin AK, Huang VL, Suganthan PN (2009) Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Transactions on Evolutionary Computation 13(2):398–417, DOI: https://doi.org/10.1109/TEVC.2008.927706
Sagaseta C (1987) Analysis of undraind soil deformation due to ground loss. Geotechnique 37(3):301–320, DOI: https://doi.org/10.1680/geot.1987.37.3.301
Samaniego E, Anitescu C, Goswami S, Nguyen-Thanh VM, Guo H, Hamdia K, Zhuang XY, Rabczuk T (2019) An energy approach to the solution of partial differential equations in computational mechanics via machine learning: Concepts, implementation and applications. Computer Methods in Applied Mechanics and Engineering 362: 112790, DOI: https://doi.org/10.1016/j.cma.2019.112790
Shi SS, Zhao RJ, Li SC, Xie XK, Li LP, Zhou ZQ, Liu HL (2019) Intelligent prediction of surrounding rock deformation of shallow buried highway tunnel and its engineering application. Tunnelling and Underground Space Technology 90:1–11, DOI: https://doi.org/10.1016/j.tust.2019.04.013
Shishegaran A, Khalili MR, Karami B, Rabczuk T, Shishegaran A (2020a) Computational predictions for estimating the maximum deflection of reinforced concrete panels subjected to the blast load. International Journal of Impact Engineering 2020(139):1–14, DOI: https://doi.org/10.1016/j.ijimpeng.2020.103527
Shishegaran A, Saeedi M, Kumar A, Ghiasinejad H (2020b) Prediction of air quality in Tehran by developing the nonlinear ensemble model. Journal of Cleaner Production 259:1–16, DOI: https://doi.org/10.1016/j.jclepro.2020.120825
Shishegaran A, Shokrollahi M, Mirnorollahi A, Shishegaran A, Mohammad Khani M (2020c) A novel ensemble model for predicting the performance of a novel vertical slot fishway. Frontiers of Structural and Civil Engineering 14(6):1418–1444, DOI: https://doi.org/10.1007/s11709-020-0664-x
Shishegaran A, Varaee H, Rabczuk T, Shishegaran G (2021) High correlated variables creator machine: Prediction of the compressive strength of concrete. Computers and Structures 247:106479, DOI: https://doi.org/10.1016/j.compstruc.2021.106479
Sirivachiraporn A, Phienwej N (2012) Ground movements in EPB shield tunneling of Bangkok subway project and impacts on adjacent buildings. Tunnelling and Underground Space Technology 30:10–24, DOI: https://doi.org/10.1016/j.tust.2012.01.003
Sivrikaya O, Toğrol E (2006) Determination of undrained strength of fine-grained soils by means of SPT and its application in Turkey. Engineering Geology 86(1):52–69, DOI: https://doi.org/10.1016/j.enggeo.2006.05.002
Soma KP, Loganathan R, Ajay V (2009) Machine learning with SVM and other kernel methods. PHI Learning Pvt. Ltd., New Delhi, India
Sowers, George B (1951) Introductory soil mechanics and foundations. The Macmillan Co., New York, NY, USA
Storn R, Price K (1997) Differential evolution — A simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization 11(4):341–359, DOI: https://doi.org/10.1023/A:1008202821328
Suwansawat S, Einstein HH (2006) Artificial neural networks for predicting the maximum surface settlement caused by EPB shield tunneling. Tunnelling and Underground Space Technology 21(2): 133–150, DOI: https://doi.org/10.1016/j.tust.2005.06.007
Talebi K, Memarian H, Rostami J, Gharahbagh EA (2015) Modeling of soil movement in the screw conveyor of the earth pressure balance machines (EPBM) using computational fluid dynamics. Tunnelling and Underground Space Technology 47:136–142, DOI: https://doi.org/10.1016/j.tust.2014.12.008
Vapnik VN (1995) The nature of statistical learning theory. Springer-Verlag, New York, NY, USA
Vinai R, Oggeri C, Peila D (2008) Soil conditioning of sand for EPB applications: A laboratory research. Tunnelling and Underground Space Technology 23(3):308–317, DOI: https://doi.org/10.1016/j.tust.2007.04.010
Wang FK, Du T (2014) Implementing support vector regression with differential evolution to forecast motherboard shipments. Expert Systems with Applications 41(8):3850–3855, DOI: https://doi.org/10.1016/j.eswa.2013.12.022
Wang J, Li L, Niu D, Tan Z (2012) An annual load forecasting model based on support vector regression with differential evolution algorithm. Applied Energy 94:65–70, DOI: https://doi.org/10.1016/j.apenergy.2012.01.010
Wang Z, Yao WJ, Cai YQ, Xu B, Fu Y, Wei G (2019) Analysis of ground surface settlement induced by the construction of a large-diameter shallow-buried twin-tunnel in soft ground. Tunnelling and Underground Space Technology 83:520–532, DOI: https://doi.org/10.1016/j.tust.2018.09.021
Wen Z, Rong X, Wang Z, Han ST, Xiong ZM, Shi YH (2021) A fast estimation method of soil discharged by an earth pressure balanced shield machine. KSCE Journal of Civil Engineering 25(6):2239–2249, DOI: https://doi.org/10.1007/s12205-021-1107-5
Zhang P, Chen R, Wu HN (2019) Real-time analysis and regulation of EPB shield steering using random forest. Automation in Construction 106:102860, DOI: https://doi.org/10.1016/j.autcon.2019.102860
Zhang F, Deb C, Lee SE, Yang J, Shah KW (2016) Time series forecasting for building energy consumption using weighted Support Vector Regression with differential evolution optimization technique. Energy and Buildings 126:94–103, DOI: https://doi.org/10.1016/j.enbuild.2016.05.028
Zhang Z, Huang M (2014) Geotechnical influence on existing subway tunnels induced by multiline tunneling in Shanghai soft soil. Computers and Geotechnics 56:121–132, DOI: https://doi.org/10.1016/j.compgeo.2013.11.008
Zhang P, Li H, Ha QP, Yin ZY, Chen RP (2020a) Reinforcement learning based optimizer for improvement of predicting tunneling-induced. Advanced Engineering Informatics 45:101097, DOI: https://doi.org/10.1016/j.aei.2020.101097
Zhang J, Sanderson AC (2009) JADE: Adaptive differential evolution with optional external archive. IEEE Transactions on Evolutionary Computation 13(5):945–958, DOI: https://doi.org/10.1109/TEVC.2009.2014613
Zhang P, Wu HN, Chen RP, Chan THT (2020b) Hybrid meta-heuristic and machine learning algorithms for tunneling-induced settlement prediction: A comparative study. Tunnelling and Underground Space Technology 99:103383, DOI: https://doi.org/10.1016/j.tust.2020.103383
Zhou C, Ding LY, He R (2013) PSO-based Elman neural network model for predictive control of air chamber pressure in slurry shield tunneling under Yangtze River. Automation in Construction 36(15): 208–217, DOI: https://doi.org/10.1016/j.autcon.2013.03.001
Zhou C, Ding LY, Skibniewski MJ, Luo HB, Zhang HT (2018) Data based complex network modeling and analysis of shield tunneling performance in metro construction. Advanced Engineering Informatics 38:168–186, DOI: https://doi.org/10.1016/j.aei.2018.06.011
Zhuang XY, Guo HW, Alajlan N, Zhu HH, Rabczuk T (2021) Deep autoencoder based energy method for the bending, vibration, and buckling analysis of Kirchhoff plates with transfer learning. European Journal of Mechanics A/Solids 87(1):104225, DOI: https://doi.org/10.1016/j.euromechsol.2021.104225
Acknowledgments
This work was supported the National Natural Science Foundation of China (42002266), by the China Postdoctoral Science Foundation Grant (2018M640488).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Wen, Z., Wang, Z., Rong, X. et al. Prediction of the Amount of Soil Discharged by an Earth Pressure Balanced Shield Machine Based on Feature Engineering. KSCE J Civ Eng 25, 4868–4886 (2021). https://doi.org/10.1007/s12205-021-0378-1
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12205-021-0378-1