References
- Amiri, M., Hasanipanah, M., & Bakhshandeh Amnieh, H. (2020). Predicting ground vibration induced by rock blasting using a novel hybrid of neural network and itemset mining. Neural Computing and Applications, 32, 1–19. https://doi.org/https://doi.org/10.1007/s00521-020-04822-w
- Bilal, N. (2014). Implementation of Sobol’s method of global sensitivity analysis to a compressor simulation model. International conference, Purdue University.
- Calvello, M., & Finno, R. J. (2004). Selecting parameters to optimize in model calibration by inverse analysis. Computers and Geotechnics, 31(5), 410–424. https://doi.org/https://doi.org/10.1016/j.compgeo.2004.03.004
- Campolongo, F., Cariboni, J. and Salteli, A. 2007. An effective screening design for sensitivity analysis of large models. Enviromental Modelling and Software. 22(10),1509–1518.
- Cao, B.-T., Freitag, S., & Meschke, G. (2016). A hybrid RNN-GPOD surrogate model for real-time settlement predictions in mechanised tunnelling. Advanced Modeling and Simulation in Engineering Sciences, 3(1), 5. https://doi.org/https://doi.org/10.1186/s40323-016-0057-9
- Chakeri, H., Hasanpour, R., Ali Hindistan, M., & Ünver, B. (2011). Analysis of interaction between tunnels in soft ground by 3D numerical modeling. Bulletin of Engineering Geology and the Environment, 70(3), 439–448. https://doi.org/https://doi.org/10.1007/s10064-010-0333-8
- Chakeri, H., & Ünver, B. (2014). A new equation for estimating the maximum surface settlement above tunnels excavated in soft ground. Environmental Earth Sciences, 71(7), 3195–3210. https://doi.org/https://doi.org/10.1007/s12665-013-2707-2
- Cheng, H., Chen, J., & Chen, G. (2019). Analysis of ground surface settlement induced by a large EPB shield tunnelling: a case study in Beijing, China. Environmental Earth Sciences, 78(20), 605. https://doi.org/https://doi.org/10.1007/s12665-019-8620-6
- Derbal, I., Bourahla, N., Mebarki, A., & Bahar, R. (2020). Neural network-based prediction of ground time history responses. European Journal of Environmental and Civil Engineering, 24(1), 123–140. https://doi.org/https://doi.org/10.1080/19648189.2017.1367727
- Do, N. A. (2014). Numerical analyses of segmental tunnel lining under static and dynamic loads [PHD thesis]. INSA.
- Duan, Z., & Sterling, R. (2002). Rapid prediction of surface settlements due to normal microtunneling operations [Paper presentation]. Proc. No-Dig, Montreal, Canada.
- Ercelebi, S. G., Copur, H., & Ocak, I. (2011). Surface settlement predictions for Istanbul Metro tunnels excavated by EPB-TBM. Environmental Earth Sciences, 62(2), 357–365. https://doi.org/https://doi.org/10.1007/s12665-010-0530-6
- Feng, X.-T., Chen, B.-R., Yang, C., Zhou, H., & Ding, X. (2006). Identification of visco-elastic models for rocks using genetic programming coupled with the modified particle swarm optimization algorithm. International Journal of Rock Mechanics and Mining Sciences, 43(5), 789–801. https://doi.org/https://doi.org/10.1016/j.ijrmms.2005.12.010
- Garitselov, O., Mohanty, S. P., & Kougianos, E. (2011). A comparative study of metamodels for fast and accurate simulation of nano-CMOS circuits. IEEE Transactions on Semiconductor Manufacturing, 25(1), 26–36. https://doi.org/https://doi.org/10.1109/TSM.2011.2173957
- Ghiasi, V., & Koushki, M. (2020). Numerical and artificial neural network analyses of ground surface settlement of tunnel in saturated soil. SN Applied Sciences, 2(5), 1–14. https://doi.org/https://doi.org/10.1007/s42452-020-2742-z
- Hasanipanah, M., Amnieh, H. B., Arab, H., & Zamzam, M. S. (2018). Feasibility of PSO–ANFIS model to estimate rock fragmentation produced by mine blasting. Neural Computing and Applications, 30(4), 1015–1024. https://doi.org/https://doi.org/10.1007/s00521-016-2746-1
- Hasanipanah, M., Armaghani, D. J., Amnieh, H. B., Majid, M. Z. A., & Tahir, M. M. (2017). Application of PSO to develop a powerful equation for prediction of flyrock due to blasting. Neural Computing and Applications, 28(S1), 1043–1050. https://doi.org/https://doi.org/10.1007/s00521-016-2434-1
- Hasanipanah, M., Noorian-Bidgoli, M., Armaghani, D. J., & Khamesi, H. (2016). Feasibility of PSO-ANN model for predicting surface settlement caused by tunneling. Engineering with Computers, 32(4), 705–715. https://doi.org/https://doi.org/10.1007/s00366-016-0447-0
- Heidari sheibani R., Zare S., Mirzaui nasirabad H., Foroughi M. (2013) Numerical Study of Face Pressure Effect on Surface Settlement in Soft Ground Mechanized Tunneling-A Case Study: Tehran Metro Line 7(in persian).
- Jebur, A. A., Atherton, W., Al Khaddar, R. M., & Loffill, E. (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, 22, 1–23. https://doi.org/https://doi.org/10.1080/19648189.2018.1531269
- Jurado-Piña, R., & Jimenez, R. (2015). A genetic algorithm for slope stability analyses with concave slip surfaces using custom operators. Engineering Optimization, 47(4), 453–472. https://doi.org/https://doi.org/10.1080/0305215X.2014.895339
- Kang, F., Li, J., & Ma, Z. (2013). An artificial bee colony algorithm for locating the critical slip surface in slope stability analysis. Engineering Optimization, 45(2), 207–223. https://doi.org/https://doi.org/10.1080/0305215X.2012.665451
- Kasper, T., & Meschke, G. (2004). A 3D finite element simulation model for TBM tunnelling in soft ground. International Journal for Numerical and Analytical Methods in Geomechanics, 28(14), 1441–1460. https://doi.org/https://doi.org/10.1002/nag.395
- Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization (PSO) [Paper presentation]. Proc. IEEE International Conference on Neural Networks, Perth, Australia.
- Khalili, A., Ahangari, K., Ghaemi, M., & Zarei, H. (2018). Introducing a new criterion for tunnel crown settlement: A case study of Chehel-Chay water conveyance tunnel. International Journal of Geotechnical Engineering, 12(3), 217–227. https://doi.org/https://doi.org/10.1080/19386362.2016.1264680
- Khatibinia, M., Salajegheh, E., Salajegheh, J., & Fadaee, M. J. (2013). Reliability-based design optimization of reinforced concrete structures including soil–structure interaction using a discrete gravitational search algorithm and a proposed metamodel. Engineering Optimization, 45(10), 1147–1165. https://doi.org/https://doi.org/10.1080/0305215X.2012.725051
- Khorasani, Emad, Masoud Zare Naghadihi, Rafael Jimenez, Sadegh Tarigh Azari, Seyed Mohammad Esmaeil Jalali, and Shokrollah Zare. 2018. “Performance analyses of tunnel boring machine by probabilistic systems approach”. Proceedings of the Institution of Civil Engineers -Geotechnical Engineering, 171(5), 422–438.
- Kim, C. Y., Bae, G. J., Hong, S. W., Park, C. H., Moon, H. K., & Shin, H. S. (2001). Neural network based prediction of ground surface settlements due to tunnelling. Computers and Geotechnics, 28(6-7), 517–547. https://doi.org/https://doi.org/10.1016/S0266-352X(01)00011-8
- Lai, H., Zheng, H., Chen, R., Kang, Z., & Liu, Y. (2020). Settlement behaviors of existing tunnel caused by obliquely under-crossing shield tunneling in close proximity with small intersection angle. Tunnelling and Underground Space Technology, 97, 103258. https://doi.org/https://doi.org/10.1016/j.tust.2019.103258
- Lambrughi, A., L. Medina Rodriguez, L., & Castellanza, R. (2012). Development and validation of a 3D numerical modal for TBM-EPB mechanized excavation. Computers and Geotechnics, 40, 97–113. https://doi.org/https://doi.org/10.1016/j.compgeo.2011.10.004.
- Lee, J.-H., & Akutagawa, S. (2009). Quick prediction of tunnel displacements using artificial neural network and field measurement results. International Journal of the JCRM, 5(2), 53–62. https://doi.org/https://doi.org/10.11187/ijjcrm.5.53.
- Mohammadi, S. D., Naseri, F., & Alipoor, S. (2015). Development of artificial neural networks and multiple regression models for the NATM tunnelling-induced settlement in Niayesh subway tunnel, Tehran. Bulletin of Engineering Geology and the Environment, 74(3), 827–843. https://doi.org/https://doi.org/10.1007/s10064-014-0660-2
- Mohanty, S. P. (2015). Nanoelectronic mixed-signal system design. McGraw-Hill Education.
- Monjezi, M., Hasanipanah, M., & Khandelwal, M. (2013). Evaluation and prediction of blast-induced ground vibration at Shur River Dam, Iran, by artificial neural network. Neural Computing and Applications, 22(7-8), 1637–1643. https://doi.org/https://doi.org/10.1007/s00521-012-0856-y
- Morris, M. D. (1991). Factorial sampling plans for preliminary computational experiments. Technometrics, 33(2), 161–174. https://doi.org/https://doi.org/10.2307/1269043
- Nagaraja, S. (2014). Certain investigations on the effect of Compression ratio over the performance and emission characteristics of CI engine with biodiesel blends.
- Ninić, J. (2016). Computational strategies for predictions of the soil-structure interaction during mechanized tunneling [PHD thesis]. RUHR University Bochum Germany.
- Ninić, J., Freitag, S., & Meschke, G. (2017). A hybrid finite element and surrogate modelling approach for simulation and monitoring supported TBM steering. Tunnelling and Underground Space Technology, 63, 12–28. https://doi.org/https://doi.org/10.1016/j.tust.2016.12.004
- Ninić, J., & Meschke, G. (2015). Model update and real-time steering of tunnel boring machines using simulation-based meta models. Tunnelling and Underground Space Technology, 45, 138–152. https://doi.org/https://doi.org/10.1016/j.tust.2014.09.013
- Ninić, J., Stascheit, J., & Meschke, G. (2013). Simulation-based steering for mechanized tunneling using an ANN-PSO-based meta-model [Paper presentation]. Proceedings of the Third International Conference on Soft Computing Technology in Civil, Structural and Environmental Engineering, Stirlingshire, Scotland.
- Okobiah, O., Mohanty, S. P., Kougianos, E., & Garitselov, O. (2012). Kriging-assisted ultra-fast simulated-annealing optimization of a clamped bitline sense amplifier [Paper presentation]. 2012 25th International Conference on VLSI Design. IEEE computer society, United states, https://doi.org/https://doi.org/10.1109/VLSID.2012.89
- Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1988). Learning representations by back-propagating errors. Cognitive Modeling, 5(3), 1.
- Saltelli, A. (2002). Sensitivity analysis for importance assessment. Risk Analysis, 22(3), 579–590. https://doi.org/https://doi.org/10.1111/0272-4332.00040
- Schanz, T., & Meier, J. (2008). Gestaltung, Validierung und Optimierung von Messprogrammen für geotechnische Aufgabenstellungen. Bautechnik, 85(5), 307–316. https://doi.org/https://doi.org/10.1002/bate.200810023
- Shi, J., Ortigao, J. A. R., & Bai, J. (1998). Modular neural networks for predicting settlements during tunneling. Journal of Geotechnical and Geoenvironmental Engineering, 124(5), 389–395. https://doi.org/https://doi.org/10.1061/(ASCE)1090-0241(1998)124:5(389)
- Song, Z. P., Ren, S. B., & Guo, Z. C. (2011). The tunnel surrounding rock parameters identification method based on PSO-ANN [Paper presentation]. Applied Mechanics and Materials Journal, 96, 637–640. https://doi.org/https://doi.org/10.4028/www.scientific.net/AMM.94-96.637
- Su, Y., Wang, G.-f., & Zhou, Q.-h. (2014). Tunnel face stability and ground settlement in pressurized shield tunnelling. Journal of Central South University, 21(4), 1600–1606. https://doi.org/https://doi.org/10.1007/s11771-014-2101-6
- Suwansawat, S., & Einstein, H. H. (2006). Artificial neural networks for predicting the maximum surface settlement caused by EPB shield tunneling. Tunnelling and Underground Space Technology, 21(2), 133–150. https://doi.org/https://doi.org/10.1016/j.tust.2005.06.007
- Tsekouras, G. J., Koukoulis, J., Nikolinakou, M. A., & Mastorakis, N. E. (2008). Prediction of face settlement during tunneling excavation using artificial neural network [Paper presentation]. WSEAS International Conference on Engineering Mechanics, Structures, Engineering Geology.
- Xia, Y-m., Tang, L., Ji, Z-y., Cheng, Y-l., & Bian, Z-k. (2015). Optimal design of structural parameters for shield cutterhead based on fuzzy mathematics and multi-objective genetic algorithm. Journal of Central South University, 22(3), 937–945. https://doi.org/https://doi.org/10.1007/s11771-015-2604-9
- Yang, H., Hasanipanah, M., Tahir, M. M., & Bui, D. T. (2019). Intelligent prediction of blasting-induced ground vibration using ANFIS optimized by GA and PSO. Natural Resources Research 29, 1–12.
- Yang, Y., & Zhang, Q. (1997). A hierarchical analysis for rock engineering using artificial neural networks. Rock Mechanics and Rock Engineering, 30(4), 207–222. https://doi.org/https://doi.org/10.1007/BF01045717
- Yang, Y.-y., & Li, H-a. (2012). Failure mechanism of large-diameter shield tunnels and its effects on ground surface settlements. Journal of Central South University, 19(10), 2958–2965. https://doi.org/https://doi.org/10.1007/s11771-012-1364-z
- Zhang, Y., Gallipoli, D., & Augarde, C. (2009). Parallel hybrid particle swarm optimization and applications in geotechnical engineering [Paper presentation]. International Symposium on Intelligence Computation and Applications. ISICA2009,5821, 466-475, Springer, Berlin, Heidelberg.
- Zhao, C., Lavasan, A. A., Barciaga, T., Zarev, V., Datcheva, M., & Schanz, T. (2015). Model validation and calibration via back analysis for mechanized tunnel simulations–The Western Scheldt tunnel case. Computers and Geotechnics, 69, 601–614. https://doi.org/https://doi.org/10.1016/j.compgeo.2015.07.003