Document Type : Original Research Paper

Authors

1 Shahrood University of Technology, Faculty of Mining, Geophysics and Petroleum Engineering, Shahrood, Iran

2 Department of Mining Engineering, Sahand University of Technology,Tabriz, Iran

Abstract

Surface settlement induced by tunneling is one of the most crucial problems in urban environments. Hence, accurate prediction of soil geotechnical properties is an important prerequisite in the minimization of it. In this research work, the amount of surface settlement is predicted using three-dimensional numerical simulation in the finite difference method and Artificial Neural Network (ANN). In order to determine the real geotechnical properties of soil layers around the tunnel; back-analysis is carried out using the optimization algorithm and monitoring data. Among the different optimization methods, genetic algorithm (GA) and particle swarm optimization (PSO) are selected, and their performance is compared. The results obtained show that the artificial neural network has a high ability with the amounts of R=0.99, RMSE=0.0117, and MSE= 0.000138 in predicting the surface settlement obtained from 150 simulations from randomly generated data. Comparing the results of back-analysis using the optimization algorithm, the genetic algorithm shows less error than the particle swarm algorithm in different initial populations. In all cases of analysis, the calculation time for both algorithms lasts about 5 minutes, which indicates the applicability of both algorithms in optimizing the parameters in mechanized tunneling in a short time.

Keywords

[1]. Mair, R., (1983), Geotechnical aspects of soft ground tunnelling.
[2]. O'REILLY, M.P. and B. New,( 1982), Settlements above tunnels in the United Kingdom-their magnitude and prediction..
[3]. Peck, R.B., (1969)Deep excavations and tunneling in soft ground. Proc. 7th ICSMFE: p. 225-290.
[4]. Bobet, A.,( 2001), Analytical solutions for shallow tunnels in saturated ground. Journal of Engineering Mechanics. 127(12): p. 1258-1266.
[5]. Chou, W.-I. and A. Bobet,( 2002), Predictions of ground deformations in shallow tunnels in clay. Tunnelling and underground space technology. 17 (1): p. 3-19.
[6]. Loganathan, N. and H. Poulos, (1998), Analytical prediction for tunneling-induced ground movements in clays. Journal of Geotechnical and geoenvironmental engineering. 124 (9): p. 846-856.
[7]. Park, K.-H., (2005) Analytical solution for tunnelling-induced ground movement in clays. Tunnelling and underground space technology. 20 (3): p. 249-261.
[8]. Ercelebi, S., H. Copur, and I. Ocak, (2011) , Surface settlement predictions for Istanbul Metro tunnels excavated by EPB-TBM. Environmental Earth Sciences. 62 (2): p. 357-365.
[9]. Melis, M., L. Medina, and J.M. (2002 ),Rodríguez, Prediction and analysis of subsidence induced by shield tunnelling in the Madrid Metro extension. Canadian Geotechnical Journal. 39 (6): p. 1273-1287.
[10]. Meng, F.-y., R.-p. Chen, and X. Kang, (2018 ), Effects of tunneling-induced soil disturbance on the post-construction settlement in structured soft soils. Tunnelling and Underground Space Technology,. 80: p. 53-63.
[11]. Barla, G., (2016 ), Applications of numerical methods in tunnelling and underground excavations: Recent trends. Rock Mechanics and Rock Engineering: From the Past to the Future, p. 29-40.
[12]. Ghiasi, V. and M. Koushki, (2020), Numerical and artificial neural network analyses of ground surface settlement of tunnel in saturated soil. SN Applied Sciences. 2: p. 1-14.
[13]. Vardakos, S., M. Gutierrez, and C. Xia, (2012), Parameter identification in numerical modeling of tunneling using the Differential Evolution Genetic Algorithm (DEGA). Tunnelling and underground space technology, 28: p. 109-123.
[14]. Phoon, K.-K. and F.H. Kulhawy, (1999), Characterization of geotechnical variability. Canadian geotechnical journal. 36 (4): p. 612-624.
[15]. Ninić, J., S. Freitag, and G. Meschke, (2017), A hybrid finite element and surrogate modelling approach for simulation and monitoring supported TBM steering. Tunnelling and Underground Space Technology. 63: p. 12-28.
[16]. Zhao, C.et al., (2015), Model validation and calibration via back analysis for mechanized tunnel simulations–The Western Scheldt tunnel case. Computers and Geotechnics. 69: p. 601-614.
[17]. Vardakos, S., (2007), Back-analysis methods for optimal tunnel design, Virginia Tech.
[18]. Swoboda, G.et al., (1999), Back analysis of large geotechnical models. International Journal for Numerical and Analytical Methods in Geomechanics. 23 (13): p. 1455-1472.
[19]. Pichler, B., R. Lackner, and H.A. Mang, (2003), Back analysis of model parameters in geotechnical engineering by means of soft computing. International journal for numerical methods in engineering. 57 (14): p. 1943-1978.
[20]. Ledesma, A., A. Gens, and E. Alonso, (1996), Estimation of parameters in geotechnical backanalysis—I. Maximum likelihood approach. Computers and Geotechnics. 18 (1): p. 1-27.
[21]. Gioda, G. and S. Sakurai, (1987), Back analysis procedures for the interpretation of field measurements in geomechanics. International Journal for Numerical and Analytical Methods in Geomechanics. 11 (6): p. 555-583.
[22]. Gens, A., A. Ledesma, and E. Alonso, (1996), Estimation of parameters in geotechnical backanalysis—II. Application to a tunnel excavation problem. Computers and Geotechnics. 18 (1): p. 29-46.
[23]. Singh, D.K., V. Aromal, and A. Mandal, (2018), Prediction of surface settlements in subway tunnels by regression analysis. International Journal of Geotechnical Engineering.
[24]. Ninić, J. and G. Meschke, (2015), Model update and real-time steering of tunnel boring machines using simulation-based meta models. Tunnelling and Underground Space Technology. 45: p. 138-152.
[25]. Cao, B.-T., S. Freitag, and G. Meschke, (2016), A hybrid RNN-GPOD surrogate model for real-time settlement predictions in mechanised tunnelling. Advanced Modeling and Simulation in Engineering Sciences. 3 (1): p. 5.
[26]. Zhang, J.r., G. Huang, and X.m. Gou, (2018), An optimum metamodel for safety control of operational subway tunnel during underpass shield tunneling. Structural Control and Health Monitoring. 25 (8): p. e2195.
[27]. Ankenman, B., B.L. Nelson, and J. Staum,( 2010), Stochastic kriging for simulation metamodeling. Operations research. 58 (2): p. 371-382.
[28]. Mahmoodzadeh, A.et al.,( 2020), Forecasting maximum surface settlement caused by urban tunneling. Automation in Construction. 120: p. 103375.
[29]. Rezaei, M. and M. Asadizadeh, (2020), Predicting unconfined compressive strength of intact rock using new hybrid intelligent models. Journal of Mining and Environment. 11 (1): p. 231-246.
[30]. Nikakhtar, L., S. Zare, and H. Mirzaei Nasirabad, (2022), Intelligent identification of soil and operation parameters in mechanised tunnelling by a hybrid model of artificial neural network-genetic algorithm (case study: Tabriz Metro Line 2). Civil Engineering and Environmental Systems. p. 1-22.
[31]. Penumadu, D. and R. Zhao, (2000), Modeling drained triaxial compression behavior of sand using ANN, in Numerical Methods in Geotechnical Engineering. p. 71-87.
[32]. Nikakhtar, L.et al., (2020), Application of ANN-PSO algorithm based on FDM numerical modelling for back analysis of EPB TBM tunneling parameters. European Journal of Environmental and Civil Engineering, p. 1-18.
[33]. Moormann, C., (2004), Analysis of wall and ground movements due to deep excavations in soft soil based on a new worldwide database. Soils and foundations. 44 (1): p. 87-98.
[34]. Jebur, A.A.et al., (2018), Artificial neural network (ANN) approach for modelling of pile settlement of open-ended steel piles subjected to compression load. European Journal of Environmental and Civil Engineering. p. 1-23.
[35]. Zhang, K.et al., (2020), Evolutionary hybrid neural network approach to predict shield tunneling-induced ground settlements. Tunnelling and Underground Space Technology. 106: p. 103594.
[36]. Moghaddasi, M.R. and M. Noorian-Bidgoli, (2018), ICA-ANN, ANN and multiple regression models for prediction of surface settlement caused by tunneling. Tunnelling and Underground Space Technology. 79: p. 197-209.
[37]. Hasanipanah, M.et al., (2016), Feasibility of PSO-ANN model for predicting surface settlement caused by tunneling. Engineering with Computers. 32 (4): p. 705-715.
[38]. Wang, Y.et al., (2020), A Novel Method for Analyzing the Factors Influencing Ground Settlement during Shield Tunnel Construction in Upper-Soft and Lower-Hard Fissured Rock Strata considering the Coupled Hydromechanical Properties. Geofluids.
[39]. Golpasand, M.-R.B., N.A. Do, and D. Dias, (2019), Impact of pre-existent Qanats on ground settlements due to mechanized tunneling. Transportation Geotechnics. 21: p. 100262.
[40]. Akbari, J. and M.S. Ayubirad, (2017), Seismic optimum design of steel structures using gradient-based and genetic algorithm methods. International Journal of Civil Engineering. 15 (2): p. 135-148.
[41]. Palizi, S. and A.S. Daryan, (2021), Critical Temperature Evaluation of Moment Frames by Means of Plastic Analysis Theory and Genetic Algorithm. Iranian Journal of Science and Technology, Transactions of Civil Engineering. p. 1-14.
[42]. Ataei, M. and M. Osanloo, (2004).Using a combination of genetic algorithm and the grid search method to determine optimum cutoff grades of multiple metal deposits. International Journal of Surface Mining, Reclamation and Environment. 18 (1): p. 60-78.
[43]. Mirjalili, S., (2019), Genetic algorithm, in Evolutionary algorithms and neural networks. Springer. p. 43-55.
[44]. Goshtasbi, K., M. Ataei, and R. Kalatehjary, ( 2008), Slope modification of open pit wall using a genetic algorithm-case study: southern wall of the 6th Golbini jajarm bauxite mine. Journal of the Southern African Institute of Mining and Metallurgy. 108 (10): p. 651-656.
[45]. Ataei, M. and F. Sereshki, (2017), Improved prediction of blast-induced vibrations in limestone mines using Genetic Algorithm. Journal of Mining and Environment. 8 (2): p. 291-304.
[46]. Khatami, S.A.et al. (2013), Artificial neural network analysis of twin tunnelling-induced ground settlements. in 2013 IEEE International Conference on Systems, Man, and Cybernetics. IEEE.
[47]. Ebtehaj, I., H. Bonakdari, and M.S. Es-haghi, (2019), Design of a hybrid ANFIS–PSO model to estimate sediment transport in open channels. Iranian Journal of Science and Technology, Transactions of Civil Engineering. 43(4): p. 851-857.
[48]. Fathi, M., A. Alimoradi, and H. Hemati Ahooi, (2021),Optimizing Extreme Learning Machine Algorithm using Particle Swarm Optimization to Estimate Iron Ore Grade. Journal of Mining and Environment. 12 (2): p. 397-411.
[49]. Yang, H.et al., (2019), Intelligent prediction of blasting-induced ground vibration using ANFIS optimized by GA and PSO. Natural Resources Research: p. 1-12.
[50]. Meulenkamp, F. and M.A. Grima, (1999), Application of neural networks for the prediction of the unconfined compressive strength (UCS) from Equotip hardness. International Journal of rock mechanics and mining sciences. 36 (1): p. 29-39.
[51]. Ornek, M.et al.,( 2012), Prediction of bearing capacity of circular footings on soft clay stabilized with granular soil. Soils and Foundations. 52 (1): p. 69-80.
[52]. Ceryan, N., U. Okkan, and A. Kesimal, (2013), Prediction of unconfined compressive strength of carbonate rocks using artificial neural networks. Environmental earth sciences. 68 (3): p. 807-819.
[53]. Armaghani, D.J.et al., (2018), Airblast prediction through a hybrid genetic algorithm-ANN model. Neural Computing and Applications. 29 (9): p. 619-629.
[54]. Kachitvichyanukul, V. (2012), Comparison of three evolutionary algorithms: GA, PSO, and DE. Industrial Engineering and Management Systems. 11 (3): p. 215-223.