[1] Iranian Code of Practice for Seismic Resistant Design of Buildings, Standard no. 2800, Fourth edition.
[2] U.B. Code, International building code, International Code Council, USA, (1997).
[3] P. Bisch, E. Carvalho, H. Degee, P. Fajfar, M. Fardis, P. Franchin, M. Kreslin, A. Pecker, P. Pinto, A. Plumier, Eurocode 8: seismic design of buildings worked examples, Luxembourg: Publications Office of the European Union, (2012).
[4] S. Qasim, I. Harahap, Geotechnical uncertainties and reliability theory applications, Int. J. Eng. Res. Technol, 1(6) (2012) 1-8.
[5] W. Gong, L. Wang, S. Khoshnevisan, C.H. Juang, H. Huang, J. Zhang, Robust geotechnical design of earth slopes using fuzzy sets, Journal of Geotechnical and Geoenvironmental Engineering, 141(1) (2015) 04014084.
[6] S. Miro, M. König, D. Hartmann, T. Schanz, A probabilistic analysis of subsoil parameters uncertainty impacts on tunnel-induced ground movements with a back-analysis study, Computers and Geotechnics, 68 (2015) 38-53.
[7] Y. Honjo, Challenges in geotechnical reliability based design, Geotechnical Safety and Risk. ISGSR 2011, (2011) 11-28.
[8] J.T. Christian, C.C. Ladd, G.B. Baecher, Reliability applied to slope stability analysis, Journal of Geotechnical Engineering, 120(12) (1994) 2180-2207.
[9] M. Oberguggenberger, W. Fellin, The fuzziness and sensitivity of failure probabilities, in: Analyzing uncertainty in civil engineering, Springer, 2005, pp. 33-49.
[10] T.J. Ross, Fuzzy logic with engineering applications, Wiley Online Library, 2004.
[11] T. Fetz, M. Oberguggenberger, J. Jager, D. Koll, G. Krenn, H. Lessmann, R.F. Stark, Fuzzy models in geotechnical engineering and construction management, Computer‐Aided Civil and Infrastructure Engineering, 14(2) (1999) 93-106.
[12] M.A. Grima, P. Bruines, P. Verhoef, Modeling tunnel boring machine performance by neuro-fuzzy methods, Tunnelling and underground space technology, 15(3) (2000) 259-269.
[13] M. Rahman, J. Wang, Fuzzy neural network models for liquefaction prediction, Soil dynamics and earthquake engineering, 22(8) (2002) 685-694.
[14] P. Provenzano, S. Ferlisi, A. Musso, Interpretation of a model footing response through an adaptive neural fuzzy inference system, Computers and Geotechnics, 31(3) (2004) 251-266.
[15] M. Meydani, G. Habibaghai, S. Katebi, An aggregated fuzzy reliability index for slope stability analysis, (2004).
[16] C. Kayadelen, O. Günaydın, M. Fener, A. Demir, A. Özvan, Modeling of the angle of shearing resistance of soils using soft computing systems, Expert Systems with Applications, 36(9) (2009) 11814-11826.
[17] J.K. Hamidi, K. Shahriar, B. Rezai, H. Bejari, Application of fuzzy set theory to rock engineering classification systems: an illustration of the rock mass excavability index, Rock mechanics and rock engineering, 43(3) (2010) 335-350.
[18] P. Bhargavi, S. Jyothi, Soil classification by generating fuzzy rules, International Journal on Computer Science and Engineering, 2(08) (2010) 2571-2576.
[19] U. MAJTS, S. Dinesh, Fuzzy modeling for contaminated soil parameters, Int J Fuzzy Syst Adv Appl, 1 (2014) 66-73.
[20] M.H. Jokar, S. Mirasi, Using adaptive neuro-fuzzy inference system for modeling unsaturated soils shear strength, Soft Computing, 22(13) (2018) 4493-4510.
[21] M.M. Hasheminejad, N. Sohankar, A. Hajiannia, Predicting the collapsibility potential of unsaturated soils using adaptive neural fuzzy inference system and particle swarm optimization, Scientia Iranica, 25(6) (2018) 2980-2996.
[22] D. Toksoz, I. Yilmaz, A fuzzy prediction approach for swell potential of soils, Arabian Journal of Geosciences, 12(23) (2019) 1-10.
[23] A. Sujatha, L. Govindaraju, N. Shivakumar, V. Devaraj, Fuzzy Expert System for Engineering Classification of Soils, in: Geotechnical Characterization and Modelling, Springer, 2020, pp. 85-101.
[24] Y. Liu, H.H. Zhang, Y. Wu, Hard or soft classification? large-margin unified machines, Journal of the American Statistical Association, 106(493) (2011) 166-177.
[25] J. Clive, M.A. Woodbury, I.C. Siegler, Fuzzy and crisp set-theoretic-based classification of health and disease, Journal of Medical Systems, 7(4) (1983) 317-332.
[26] G. Metternicht, Categorical fuzziness: a comparison between crisp and fuzzy class boundary modelling for mapping salt-affected soils using Landsat TM data and a classification based on anion ratios, Ecological Modelling, 168(3) (2003) 371-389.
[27] J. Jara, R. Acevedo-Crespo, Crisp classifiers vs. fuzzy classifiers: A statistical study, in: International Conference on Adaptive and Natural Computing Algorithms, Springer, 2009, pp. 440-447.
[28] E. Onieva, P. Lopez-Garcia, A. Masegosa, E. Osaba, A. Perallos, A comparative study on the performance of evolutionary fuzzy and crisp rule based classification methods in congestion prediction, Transportation Research Procedia, 14 (2016) 4458-4467.
[29] M.R. Chenaghlou, A.A. Hamed, 03.30: Connection classification for a space structure jointing system, ce/papers, 1(2-3) (2017) 746-755.
[30] E. Muchai, L. Odongo, J. Kahiri, Comparison of Crisp and Fuzzy Classification Trees Using Chi-Squared ImpurityMeasure on Simulated Data.
[31] G. Klir, B. Yuan, Fuzzy sets and fuzzy logic, Prentice hall New Jersey, 1995.
[32] K. Ishihara, A.M. Ansal, Dynamic behaviour of soils soil amplification and soil structure interaction. Final report, (1982).
[33] N. Hasancebi, R. Ulusay, Empirical correlations between shear wave velocity and penetration resistance for ground shaking assessments, Bulletin of Engineering Geology and the Environment, 66(2) (2007) 203-213.
[34] Ü. Dikmen, Statistical correlations of shear wave velocity and penetration resistance for soils, Journal of Geophysics and Engineering, 6(1) (2009) 61-72.
)