Fuzzy classification of soils based on Iranian standard of 2800

Document Type : Research Article

Authors

1 Ph.D. Candidate, Civil Engineering Faculty, Sahand University of Technology (SUT), Tabriz

2 Civil Engineering Faculty, Sahand University of Technology, Tabriz

3 Civil Engineering Faculty, Sahand University of Technology, SUT, Tabriz

Abstract

Soil classification is a function of a region’s geological conditions, which according to the Iranian earthquake standard of 2800, it is consequently a function of average shear wave velocity, as well as average SPT blow count, and average undrained shear strength of cohesive soils in different layers for up to 30 meters depth. Boundaries of these geotechnical parameters are often defined as different crisp values in the earthquake design codes. Because of the uncertainties in the mentioned parameters and also the difference between values of these parameters in the real material and values obtained from the experimental tests for the determination of these parameters, statistical and probabilistic methods is needed. Due to the computational complexity of statistical and probabilistic methods, in this research, a fuzzy inference system has been used for the decision of the classification of soil type, which can consider uncertainties without the need for complex mathematical calculations. For this purpose, after defining the effective parameters for determining the soil type, triangular membership functions were selected for them, and finally, a fuzzy inference system was designed. According to the results, the proposed model provides more accuracy than the standard in the boundaries between two successive soil classes. Also, when the values of the parameters are far from the boundaries between successive soil classes, the fuzzy inference system and the standard provide the same answers.

Keywords

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References

[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.
Amirkabir Journal of Civil Engineering
Volume 54, Issue 10
January 2023
Pages 4035-4052

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