References
- Atanassov, K. T. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems, 20(1), 87–96. doi:https://doi.org/10.1016/S0165-0114(86)80034-3
- Deere, D. U., Hendron, A. J., Patton, F. D., & Cording, E. J. (1967). Design of surface and near surface construction in rock. In C. Fairhurst (Ed.), Failure and breakage of rock, proc. 8th U.S. symp. Rock mech. (pp. 237–302). New York: Soc. Min. Engrs, Am. Inst. Min. Metall. Petrolm Engrs.
- Dice, L. R. (1945). Measures of the amount of ecologic association between species. Ecology, 26(3), 297–302. doi:https://doi.org/10.2307/1932409
- Farmer, I. W., & Glossop, N. H. (1980). Mechanics of disc cutter penetration. Tunnels and Tunnelling, 12(6), 22–25.
- Gao, L., & Li, X. B. (2015). Utilizing partial least square and support vector machine for TBM penetration rate prediction in hard rock conditions. Journal of Central South University, 22(1), 290–295. doi:https://doi.org/10.1007/s11771-015-2520-z
- Ghasemi, E., Yagiz, S., & Ataei, M. (2014). Predicting penetration rate of hard rock tunnel boring machine using fuzzy logic. Bulletin of Engineering Geology and the Environment, 73(1), 23–35. doi:https://doi.org/10.1007/s10064-013-0497-0
- Grima, M. A., Bruines, P. A., & Verhoef, P. N. W. (2000). Modeling tunnel boring machine performance by neuro-fuzzy methods. Tunnelling and Underground Space Technology, 15(3), 259–269. doi:https://doi.org/10.1016/S0886-7798(00)00055-9
- Gong, Q. M., & Zhao, J. (2009). Development of a rock mass characteristics model for TBM penetration rate prediction. International Journal of Rock Mechanics and Mining Science & Geomechanics Abstracts, 46(1), 8–18. doi:https://doi.org/10.1016/j.ijrmms.2008.03.003
- Hamidi, J. K., Shahriar, K., Rezai, B., & Rostami, J. (2010). Performance prediction of hard rock TBM using rock mass rating (RMR) system. Tunnelling and Underground Space Technology, 25(4), 333–345. doi:https://doi.org/10.1016/j.tust.2010.01.008
- Hassanpour, J., Rostami, J., & Zhao, J. (2011). A new hard rock TBM performance prediction model for project planning. Tunnelling and Underground Space Technology, 26(5), 595–603. doi:https://doi.org/10.1016/j.tust.2011.04.004
- Hoseinie, S. H., Ataei, M., & Osanloo, M. (2009). A new classification system for evaluating rock penetrability. International Journal of Rock Mechanics and Mining Science & Geomechanics Abstracts, 46(8), 1329–1340. doi:https://doi.org/10.1016/j.ijrmms.2009.07.002
- Huang, F., Shen, J., Cai, M., & Xu, C. (2019). An empirical UCS model for anisotropic blocky rock masses. Rock Mechanics and Rock Engineering, 1–13. doi:https://doi.org/10.1007/s00603-019-01771-2
- Li, D., Zeng, W., & Li, J. (2015). New distance and similarity measures on hesitant fuzzy sets and their applications in multiple criteria decision making. Engineering Applications of Artificial Intelligence, 40, 11–16. doi:https://doi.org/10.1016/j.engappai.2014.12.012
- Mikaeil, R., Naghadehi, M. Z., & Sereshki, F. (2009). Multifactorial fuzzy approach to the penetrability classification of TBM in hard rock conditions. Tunnelling and Underground Space Technology, 24(5), 500–505. doi:https://doi.org/10.1016/j.tust.2008.12.007
- Nelson, P. P. (2013). TBM performance analysis with reference to rock properties. Comprehensive Rock Engineering, 4, 261–291. doi:https://doi.org/10.1016/0148-9062(94)91276-9
- Ozdemir, L. (1977). Development of theoretical equations for predicting tunnel boreability (Doctoral dissertation), T-1969, Colorado School of Mines, Golden, CO, USA, pp. 10–19.
- Rostami, J., Ozdemir, L., & Nilsen, B. (1996). Comparison between CSM and NTH hard rock TBM performance prediction models. In Proceedings, The Annual Conference of the Institution of Shaft Drilling Technology (ISDT), Las Vegas, pp. 11.
- Shen, J., Wan, L., & Zuo, J. (2019). Non-linear shear strength model for Coal Rocks. Rock Mechanics and Rock Engineering, 1–10. doi:https://doi.org/10.1007/s00603-019-01775-y
- Terzaghi, K. (1946). Rock defects and loads on tunnel supports. In Proctor RV, White T (eds), Rock tunneling with steel supports (Vol. 1, pp. 17–99). Youngstown, Ohio: Commercial Shearing & Stamping Co.
- Torabi, S. R., Shirazi, H., Hajali, H., & Monjezi, M. (2013). Study of the influence of geotechnical parameters on the TBM performance in Tehran–Shomal highway project using ANN and SPSS. Arabian Journal of Geosciences, 6(4), 1215–1227. doi:https://doi.org/10.1007/s12517-011-0415-3
- Wickham, G. E., Tiedemann, H. R., & Skinner, E. H. (1972). Support determination based on geologic predictions. In K. S. Lane & L. A. Garfield (Eds.), Proc. North American Rapid Excav. Tunneling Conf., Chicago, (pp. 43–64). New York: Soc. Min. Engrs, Am. Inst. Min. Metall. Petrolm Engrs. doi:https://doi.org/10.1016/0148-9062(75)90446-5
- Xu, Z. S., & Yager, R. R. (2006). Some geometric aggregation operators based on intuitionistic fuzzy sets. International Journal of General Systems, 35, (4), 417–433. doi:https://doi.org/10.1080/03081070600574353
- Yagiz, S. (2002). Development of rock fracture and brittleness indices to quantify the effects of rock mass features and toughness in the CSM Model basic penetration for hard rock tunneling machines (Doctoral disssertation), Department of Mining and Earth Systems Engineering, Colorado School of Mines, Golden, CO, USA, pp. 10–72.
- Yagiz, S. (2008). Utilizing rock mass properties for predicting TBM performance in hard rock condition. Tunnelling and Underground Space Technology, 23(3), 326–339. doi:https://doi.org/10.1016/j.tust.2007.04.011
- Yagiz, S., Gokceoglu, C., Sezer, E., & Iplikci, S. (2009). Application of two non-linear prediction tools to the estimation of tunnel boring machine performance. Engineering Applications of Artificial Intelligence, 22(4/5), 808–814. doi:https://doi.org/10.1016/j.engappai.2009.03.007
- Ye, J. (2012a). Multicriteria decision-making method using the Dice similarity measure based on the reduct intuitionistic fuzzy sets of interval-valued intuitionistic fuzzy sets. Applied Mathematical Modelling, 36(9), 4466–4472. doi:https://doi.org/10.1016/j.apm.2011.11.075
- Ye, J. (2012b). Multicriteria group decision-making method using vector similarity measures for trapezoidal intuitionistic fuzzy numbers. Group Decision and Negotiation, 21(4), 519–530. doi:https://doi.org/10.1007/s10726-010-9224-4
- Yong, R., Ye, J., Liang, Q. F., Huang, M., & Du, S. G. (2018). Estimation of the joint roughness coefficient (JRC) of rock joints by vector similarity measures. Bulletin of Engineering Geology and the Environment, 77(2), 735–749. doi:https://doi.org/10.1007/s10064-016-0947-6
- Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–356. doi:https://doi.org/10.1016/S0019-9958(65)90241-X