The Study of the Liquefaction Probability and Estimation of the Relative Importance of Effective Parameters Using Fuzzy Clustering and Genetic Programming

Authors

1 Department of Civil Engineering, Islamic Azad University, Tabriz branch

2 Department of Industrial Engineering, Islamic Azad University, Zanjan branch

Abstract

During several past decades, many researchers have conducted studies to develop the relationship between liquefaction resistance and seismic parameters of various soils. One of the methods to assess liquefaction which is widely used is the stress-based method, which was presented for the first time by Seed and Idriss (1971) and Whitman (1971). That is an experimental method and is based on experimental and local observations.
Anyway, the relationships developed by the use of the traditional method based on experimental data are limited and don’t provide a proper and stable prediction, they also don't show major weaknesses in mathematical relations, complete structure and heterogeneous space of soil and liquefaction, while soils have quite complicated structures, there is simpler methods of examination that allow unlimited development of a model for all systems, such as genetic programming (GP), artificial neural networks (ANNS) and Neuro-fuzzy inference system (ANFIS) (Shahin et al., 2003). In this study, using fuzzy clustering, it has been initially tried to divide data to groups whose members are similar and have relatively same properties. So data with similar geotechnical and seismic properties can be placed in a cluster and hence, a closer study of the data to better understand the factors affecting soil liquefaction can be possible. Next, a model was presented for time estimation of the soil liquefaction probability using the method of genetic programming. Finally, considering the results of the conducted clustering and the study of data in each cluster, the sensitivity of parameters affecting soil liquefaction and their relative importance were analyzed using nonlinear methods.

Keywords


Bezdek J, “Pattern Recognition with Fuzzy Objective Function Algorithms”, Plenum Press, New York, 1981.
Bezdek J, Hathaway R, “Recent convergence results for the fuzzy c-means clustering algorithms”, Journal of Classification, 1988, 5 (2), 237-247.
Castro G et al, “Liquefaction induced by cyclic loading”, Winchester, Mass: Geotechnical Engineers, 1982.
Cetin KO, et al, “Standard penetration test-based probabilistic and deterministic assessment of seismic soil liquefaction potential”, Geotechnical and Geoenvironmental Engineering, Vol, 2004, 130, No 12.
Chang Muhsiung et al, “Comparison of SPT-N-based analysis methods in evaluation of liquefaction potential during the 1999 Chi-chi earthquake in Taiwan”, Computers and Geotechnics, 2011, 38,393-406.
Chen YR, SC Hsieh, Chen JW, Shih C, “Energy-based probabilistic evaluation of soil liquefaction”, Soil Dynamics and Earthquake Engineering, 2005, 25 55-68.
Chen, O-tani H, Hori M, “Stability analysis of soil liquefaction using a finite element method based on particle discretization scheme”, Computers and Geotechnics, 2015, 67 (2015) 64-72
Chiu S, “Fuzzy model identification based on cluster estimation”, J. Intel. Fuzzy System, 1994, 2, 267-278.
Dunn J, “A fuzzy relative of the Isodata process and its use in detecting compact, well-separated clusters”, Journal of Cybernetics, 1973, 3 (3), pp. 32-57.
Gandomi AH, Alavi AH, “Multi-stage genetic programming: a new strategy to nonlinear system modeling”, Information Sciences, 2011, 181 (23), 5227-5239.
Green R, et al, “An Energy-based excess pour pressure generation model for cohesionless soils”. Proceedings of the John Booker Memorial Symposium Sydney, New South Wales, Australia, November, 2000, 16-17.
Guoxing Chen, Lingyu Xu, Mengyun Kong, Xiaojun Li, “Calibration of a CRRmodel based on an expanded SPT-based database for assessing soil liquefaction potential”, Engineering Geology, 2015 196, 305-312.
Hadi Shahir, Ali Pak,, Mahdi Taiebat , Boris Jeremic, “Evaluation of variation of permeability in liquefiable soil under earthquake loading”, Computers and Geotechnics, 2012, 40, 74-88.
Hanna Adel M, et al, “Neural Network model for liquefaction potential in soil deposits using Turkey and Taiwan earthquake data”, Soil Dynamics and Earthquake Engineering, 2006, 27,521-540.
Holland JH, “Adaption in Natural and Artificial Systems” University of Michigan Press, Ann Arbor, 1975, 228pp.
Ishihara K, “Soil behavior in earthquake geotechnics". New York: Oxford University Press, 1996.
Jafarian Y, Kermani E, Baziar MH, “Empirical predictive model for the  Vmax/amax ratio of strong ground motions using genetic programming”, Computers and Geosciences, 2011a, 36 (12),1523-1531.
Javadi A, Rezania M, Mousavinezhad M, “Evaluation of liquefaction induced lateral displacements using genetic programming”, Computers and Geotechnics, 2016, 33, 222-233.
J. Geotechnical Engineering 1985, 10.1061/ (ASCE), 733- 9410 (1985) 111:12 (1425), 1425-1445.
Kayadelen C," Estimation of effective stress parameter of unsaturated soils by using artificial neural networks". International Journal for Numerical and Analytical Methods in Geomechanics, 2008, 32, 1087-1106.
Kayadelen C, Günaydın O, Fener M, Demir A, Özvan A, "Modeling of the angle of shearing resistance of soils using soft computing systems". Expert Systems with Applications, 2009, 36, 11814-11826.
Koza JR, "Genetic Programming", www.genetic-programming.com,The home page of John R. Koza at Genetic Programming, 2008.
Koza JR, Poli R, “Genetic programming. In: Burke E, Kendall G. (Eds.), Search Methodologies; Introductory Tutorialsin Optimization and Decision Support Techniques”, Springer Science +Business Media, pp. 2005, 127-164.
Koza JR, “Genetic Programming: On the Programming of Computers by Means of Natural Selection”.MIT Press, Cambridge, MA, 1992, 813pp.
Liong SY, Gautam T R, Khu ST, Babovic V, Keijzer M, Muttil N, "Genetic programming, A new paradigm in rainfall runoff modeling", J. Am. Water Response Association, 2003, 38 (3), 705-718.
Muduli Pradyut Kumar, Das Sarat Kumar, “Model uncertainty of SPT-based method for evaluation of seismic soil liquefaction potential using multi-gene genetic programming”, Soils and Foundations, 2015, 2015;55 (2): 258-275.
NCEER, “Proceeding of the NCEER Workshop on Evaluation of Liquefaction Resistance of Soils, (T.L. Youd and Idriss I M, eds.), Technical Report NCEER-97-0022, National Center for Earthquake Engineering Research”, State University of New York at Buffalo, 1997, 276pp.
Olsen RS, Koester JP, "Prediction of liquefaction resistance using the CPT”, In International symposium on cone penetration testing, CPT 95 Linkoping, Sweden, 1995, pp. 251-256.
Rezania M, Javadi A, “An ewgenetic programming model for predicting settlements of shallow foundations”, Canadian Geotechnical Journal, 2017, 44(12), 1462-1473.
Ruspini E, “A new approach to clustering”, Information and Control, 1969, 15, pp. 22-32.
Samui Pijush, R Hariharan, “A unified classification model for modeling of seismic liquefaction potential of soil based on CPT”, Journal of Advanced Research, 2004.
Seed B, Tokimatsu H, Harder KL, Chung R, “Influence of SPT Procedures in Soil Liquefaction Resistance Evaluations”.
Seed HB, Idriss IM, “Simplified Procedure for Evaluating Soil Liquefaction Potential”, Journal of the Soil Mechanics and Foundations Division, 1971, 97 (SM9), pp1249-1273.
Seed HB, Idriss IM, Arrango, “Evaluation of liquefaction potential using field data”, Journal of Geotechnical Engineering, ASCE, 1983, 109, 458-484.
Shahin MA, Maier HR, Jaksa MB, “Settlement prediction of shallow foundations on granular soils using B-spline neuro fuzzy models". Computers and Geotechnics, 2003, 30, 637-647.
Torres RS, Falcao, Gonc alves AX, Papa MA, Zhang JP, Fan W, “A genetic programming framework for content-based image retrieval”, Pattern Recognition, 2009, 42 (2), 283e292.
Tütmez B, Tercan AE, "Spatial estimation of some mechanical properties of rocks by fuzzy modeling". Computers and Geotechnics, 2007, 34, 10-18.
Whitman RV, “Resistance of Soil to Liquefaction and Settlement”, Soils and Foundations, 1971, 11 (4), pp59-68.
Yager R, Filev D, “Generation of fuzzy rules by mountain clustering”, J. Intel. Fuzzy Systems, 1994, 2, 209-219.
Youd TL, Idriss IM, Andrus RD, Arango I, Castro G, Christian J T, Dobry R, Finn W D L, Harder L F, Hynes M E, Ishihara K, Koester J P, Liao S C, Marcuson W F, Martin GR, Mitchell JK, Moriwaki Y, Power MS, Robertson PK, Seed RB, Stokoe KH,” Liquefaction Resistance of Soils: Summary Report from the 1996 NCEER and 1998 NCEER/NSF Workshops on Evaluation of Liquefaction Resistance of Soils”, Journal of Geotechnical and Geoenvironmental Engineering, 2011, 127 (4), pp297-313.
Zeghal Mourad, Elgamal Ahmet-W, “Analysis of site liquefaction using earthquake records”, Geotechnical Engineering, Vol, 1994, 120, No.6046.
Zhang CH, Juang JR, Martin HW, Huang, “Inter-region variability of Robertson and Wride method for liquefaction hazard analysis”, Engineering Geology, 2016, 203 (2016) 191-203.