Soft computing approach for prediction of surface settlement induced by earth pressure balance shield tunneling

Adoko, 2013, Predicting tunnel convergence using multivariate adaptive regression spline and artificial neural network, Tunnelling and Underground Space Technology, 38, 368, 10.1016/j.tust.2013.07.023 Adoko, 2011, A fuzzy model for high-speed railway tunnel convergence prediction in weak rock, Electronic Journal of Geotechnical Engineering, 16, 275 Ahangari, 2015, Estimation of tunnelling-induced settlement by modern intelligent methods, Soils and Foundations, 55, 737, 10.1016/j.sandf.2015.06.006 Bilgin, N., Ozbayir, T., Sozak, N., & Eyigun, Y. (2009). Factors affecting the economy and the efficiency of metro tunnel drivage with two TBM’s in Istanbul in very fractured rock. In ITA-World Tunnel Congress. Boser, B. E., Guyon, I. M., & Vapnik, V. N. (1992). A training algorithm for optimal margin classifiers. In Proceedings of the fifth annual workshop on Computational learning theory. Pittsburgh, Pennsylvania, USA. New York, USA: ACM Press (pp. 144–152). Bouayad, 2015, Assessment of ground surface displacements induced by an earth pressure balance shield tunneling using partial least squares regression, Environmental Earth Sciences, 73, 7603, 10.1007/s12665-014-3930-1 Chakeri, 2011, Analysis of interaction between tunnels in soft ground by 3D numerical modeling, Bulletin of Engineering Geology and the Environment, 70, 439, 10.1007/s10064-010-0333-8 Chapman, 2007, Investigating ground movements caused by the construction of multiple tunnels in soft ground using laboratory model tests, Canadian Geotechnical Journal, 44, 631, 10.1139/t07-018 Chen, 2019, Reliability assessment on stability of tunneling perpendicularly beneath an existing tunnel considering spatial variabilities of rock mass properties, Tunnelling and Underground Space Technology, 88, 276, 10.1016/j.tust.2019.03.013 Chen, T., & Guestrin, C. (2016). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining (pp. 785–794). ACM. Cheng, 2008, Evolutionary fuzzy neural inference system for decision making in geotechnical engineering, Journal of Computing in Civil Engineering, 22, 272, 10.1061/(ASCE)0887-3801(2008)22:4(272) Ching, 2014, Transformations and correlations among some clay parameters—The global database, Canadian Geotechnical Journal, 51, 663, 10.1139/cgj-2013-0262 Chou, 2002, Predictions of ground deformations in shallow tunnels in clay, Tunnelling and Underground Space Technology, 17, 3, 10.1016/S0886-7798(01)00068-2 Cortes, 1995, Support-vector networks, Machine Learning, 20, 273, 10.1007/BF00994018 D’Ignazio, 2016, Correlations for undrained shear strength of Finnish soft clays, Canadian Geotechnical Journal, 53, 1628, 10.1139/cgj-2016-0037 Ding, 2017, Prediction methods on tunnel-excavation induced surface settlement around adjacent building, Geomechanics and Engineering, 12, 185, 10.12989/gae.2017.12.2.185 Ercelebi, 2011, Surface settlement predictions for Istanbul Metro tunnels excavated by EPB-TBM, Environmental Earth Sciences, 62, 357, 10.1007/s12665-010-0530-6 Fang, 2019, Model tests on longitudinal surface settlement caused by shield tunnelling in sandy soil, Sustainable Cities and Society, 47, 10.1016/j.scs.2019.101504 Friedman, 1991, Multivariate adaptive regression splines, The Annals of Statistics, 19, 1 Goh, 2018, Determination of earth pressure balance tunnel-related maximum surface settlement: A multivariate adaptive regression splines approach, Bulletin of Engineering Geology and the Environment, 77, 489, 10.1007/s10064-016-0937-8 Goh, 2017, Evaluating stability of underground entry-type excavations using multivariate adaptive regression splines and logistic regression, Tunnelling and Underground Space Technology, 70, 148, 10.1016/j.tust.2017.07.013 Gong, 2014, Optimization of site exploration program for improved prediction of tunneling-induced ground settlement in clays, Computers and Geotechnics, 56, 69, 10.1016/j.compgeo.2013.10.008 Gunn, 1998, Support vector machines for classification and regression, ISIS technical report, 14, 5 Huang, 2015, Simplified procedure for finite element analysis of the longitudinal performance of shield tunnels considering spatial soil variability in longitudinal direction, Computers and Geotechnics, 64, 132, 10.1016/j.compgeo.2014.11.010 Huang, 2018, Evaluation of train-induced settlement for metro tunnel in saturated clay based on an elastoplastic constitutive model, Underground Space, 3, 109, 10.1016/j.undsp.2017.10.001 Hulme, 1999, Tunnelling projects in Singapore: An overview, Tunnelling and Underground Space Technology, 14, 409, 10.1016/S0886-7798(00)00004-3 Izumi, C., Khatri, N. N., Norrish, A., & Davies, R. (2000). Stability and settlement due to bored tunneling for LTA, NEL. In Proceedings of International conference on tunnels and underground structures, Singapore (pp. 555–560). Jekabsons, 2010 Kim, 2001, Neural network based prediction of ground surface settlements due to tunnelling, Computers and Geotechnics, 28, 517, 10.1016/S0266-352X(01)00011-8 Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. In Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence (IJCAI), Montreal, Quebec, Canada, vol. 14, No.2, pp. 1137–1145. Kohestani, 2017, Prediction of maximum surface settlement caused by earth pressure balance shield tunneling using random forest, Journal of AI and Data Mining, 5, 127 Lambrughi, 2012, Development and validation of a 3D numerical model for TBM–EPB mechanised excavations, Computers and Geotechnics, 40, 97, 10.1016/j.compgeo.2011.10.004 Lessmann, 2015, Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research, European Journal of Operational Research, 247, 124, 10.1016/j.ejor.2015.05.030 Loganathan, 1998, Analytical prediction for tunneling-induced ground movements in clays, Journal of Geotechnical and Geoenvironmental Engineering, 124, 846, 10.1061/(ASCE)1090-0241(1998)124:9(846) Lu, 2019, Centrifuge modeling of tunneling-induced ground surface settlement in sand, Underground Space, 4, 302, 10.1016/j.undsp.2019.03.007 Mahdevari, 2012, Prediction of tunnel convergence using artificial neural networks, Tunnelling and Underground Space Technology, 28, 218, 10.1016/j.tust.2011.11.002 Mahdevari, 2012, Application of artificial intelligence algorithms in predicting tunnel convergence to avoid TBM jamming phenomenon, International Journal of Rock Mechanics and Mining Sciences, 55, 33, 10.1016/j.ijrmms.2012.06.005 Mair, 1993, Subsurface settlement profiles above tunnels in clays, Geotechnique, 43, 315, 10.1680/geot.1993.43.2.315 Marshall, 2012, Tunnels in sands: The effect of size, depth and volume loss on greenfield displacements, Géotechnique, 62, 385, 10.1680/geot.10.P.047 Mroueh, 2002, Three-dimensional finite element analysis of the interaction between tunneling and pile foundations, International Journal for Numerical and Analytical Methods in Geomechanics, 26, 217, 10.1002/nag.194 Nanni, 2009, An experimental comparison of ensemble of classifiers for bankruptcy prediction and credit scoring, Expert Systems With Applications, 36, 3028, 10.1016/j.eswa.2008.01.018 Neaupane, 2006, Prediction of tunneling-induced ground movement with the multi-layer perceptron, Tunnelling and Underground Space Technology, 21, 151, 10.1016/j.tust.2005.07.001 Ng, 2005, Three-dimensional ground settlements and stress-transfer mechanisms due to open-face tunnelling, Canadian Geotechnical Journal, 42, 1015, 10.1139/t05-025 Ocak, 2009, Environmental effects of tunnel excavation in soft and shallow ground with EPBM: The case of Istanbul, Environmental Earth Sciences, 59, 347, 10.1007/s12665-009-0032-6 Ocak, 2013, Calculation of surface settlements caused by EPBM tunneling using artificial neural network, SVM, and Gaussian processes, Environmental Earth Sciences, 70, 1263, 10.1007/s12665-012-2214-x Park, 2005, Analytical solution for tunnelling-induced ground movement in clays, Tunnelling and Underground Space Technology, 20, 249, 10.1016/j.tust.2004.08.009 Rodriguez, 2009, Sensitivity analysis of k-fold cross validation in prediction error estimation, IEEE Transactions on Pattern Analysis And Machine Intelligence, 32, 569, 10.1109/TPAMI.2009.187 Samui, 2008, Prediction of friction capacity of driven piles in clay using the support vector machine, Canadian Geotechnical Journal, 45, 288, 10.1139/T07-072 Samui, 2008, Support vector machine applied to settlement of shallow foundations on cohesionless soils, Computers and Geotechnics, 35, 419, 10.1016/j.compgeo.2007.06.014 Santos, 2008, Artificial neural networks analysis of São Paulo subway tunnel settlement data, Tunnelling and Underground Space Technology, 23, 481, 10.1016/j.tust.2007.07.002 Sharma, 1999, Geological and geotechnical features of Singapore: An overview, Tunnelling and Underground Space Technology, 14, 419, 10.1016/S0886-7798(00)00005-5 Shirlaw, J. N., Ong, J. C. W., Rosser, H. B., Tan, C. G., Osborne, N. H., & Heslop, P. E. (2003). Local settlements and sinkholes due to EPB tunnelling. In Proceedings of the Institution of Civil Engineers-Geotechnical Engineering, 156(4), 193–211. Suwansawat, 2006, Artificial neural networks for predicting the maximum surface settlement caused by EPB shield tunneling, Tunnelling and Underground Space Technology, 21, 133, 10.1016/j.tust.2005.06.007 Verruijt, 1996, Surface settlements due to deformation of a tunnel in an elastic half place, Géotechnique, 46, 753, 10.1680/geot.1996.46.4.753 Wang, 2012, Deformation prediction model of surrounding rock based on GA-LSSVM-Markov, Natural Science, 4, 85, 10.4236/ns.2012.42013 Wong, 2015, Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation, Pattern Recognition, 48, 2839, 10.1016/j.patcog.2015.03.009 Xiang, 2018, Application of transparent soil model test and DEM simulation in study of tunnel failure mechanism, Tunnelling and Underground Space Technology, 74, 178, 10.1016/j.tust.2018.01.020 Xie, 2018, Parametric analysis of mixshield tunnelling in mixed ground containing mudstone and protection of adjacent buildings: Case study in Nanning metro, European Journal of Environmental and Civil Engineering, 22, s130, 10.1080/19648189.2017.1359113 Xu, 2011, Grey correlation-hierarchical analysis for metro-caused settlement, Environmental Earth Sciences, 64, 1249, 10.1007/s12665-011-0940-0 Yao, 2010, Tunnel surrounding rock displacement prediction using support vector machine, International Journal of Computational Intelligence Systems, 3, 843 Zhang, 2013, Multivariate adaptive regression splines for analysis of geotechnical engineering systems, Computers and Geotechnics, 48, 82, 10.1016/j.compgeo.2012.09.016 Zhang, 2016, Multivariate adaptive regression splines and neural network models for prediction of pile drivability, Geoscience Frontiers, 7, 45, 10.1016/j.gsf.2014.10.003 Zhang, 2015, Assessment of soil liquefaction based on capacity energy concept and multivariate adaptive regression splines, Engineering Geology, 188, 29, 10.1016/j.enggeo.2015.01.009 Zhang, W. G., Wu, C. Z., Li, Y. Q., Wang, L., & Samui, P. (2021). Assessment of pile drivability using random forest regression and multivariate adaptive regression splines. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 15(1), 27–40. Zhang, 2019, A Multivariate Adaptive Regression Splines model for determining horizontal wall deflection envelope for braced excavations in clays, Tunnelling and Underground Space Technology, 84, 461, 10.1016/j.tust.2018.11.046 Zhang, 2020, State-of-the-art review of soft computing applications in underground excavations, Geoscience Frontiers, 11, 1095, 10.1016/j.gsf.2019.12.003 Zhang, 2017, Multivariate adaptive regression splines for inverse analysis of soil and wall properties in braced excavation, Tunnelling and Underground Space Technology, 64, 24, 10.1016/j.tust.2017.01.009 Zhou, 2015, Comparative performance of six supervised learning methods for the development of models of hard rock pillar stability prediction, Natural Hazards, 79, 291, 10.1007/s11069-015-1842-3