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Evaluation of liquefaction potential of soil based on standard penetration test using multi-gene genetic programming model

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Języki publikacji
EN
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EN
This paper discusses the evaluation of liquefaction potential of soil based on standard penetration test (SPT) dataset using evolutionary artificial intelligence technique, multi-gene genetic programming (MGGP). The liquefaction classification accuracy (94.19%) of the developed liquefaction index (LI) model is found to be better than that of available artificial neural network (ANN) model (88.37%) and at par with the available support vector machine (SVM) model (94.19%) on the basis of the testing data. Further, an empirical equation is presented using MGGP to approximate the unknown limit state function representing the cyclic resistance ratio (CRR) of soil based on developed LI model. Using an independent database of 227 cases, the overall rates of successful prediction of occurrence of liquefaction and non-liquefaction are found to be 87, 86, and 84% by the developed MGGP based model, available ANN and the statistical models, respectively, on the basis of calculated factor of safety (Fs) against the liquefaction occurrence. Key words: liquefaction index, standard penetration test, limits state function, artificial intelligence, multi-gene genetic programming, factor of safety
Czasopismo
Rocznik
Strony
529--543
Opis fizyczny
Bibliogr. 29 poz.
Twórcy
autor
  • Department of Civil Engineering, National Institute of Technology, Rourkela, India
autor
  • Department of Civil Engineering, National Institute of Technology, Rourkela, India
Bibliografia
  • Alavi, A.H., P. Aminian, A.H. Gandomi, and M.A. Esmaeilli (2011), Genetic-based modeling of uplift capacity of suction caissons, Expert Syst. Appl. 38, 10, 12608-12618, DOI: 10.1016/j.eswa.2011.04.049.
  • Cetin, K.O., R.B. Seed, A.D. Kiureghian, K. Tokimatsu, L.F. Harder, Jr., R.E. Kayen, and R.E.S. Moss (2004), Standard penetration test-based probabilistic and deterministic assessment of seismic soil liquefaction potential, J. Geotech. Geoenviron. Eng. 130, 12, 1314-1340, DOI: 10.1061/(ASCE) 1090-0241(2004)130:12(1314).
  • Das, S.K., and P.K. Basudhar (2008), Prediction of residual friction angle of clays using artificial neural network, Eng. Geol. 100, 3-4, 142-145, DOI: 10.1016/j.enggeo.2008.03.001.
  • Das, S.K., and P.K. Muduli (2011), Evaluation of liquefaction potential of soil using genetic programming. In: Proc. Golden Jubilee Indian Geotechnical Conference, 15-17 December 2011, Kochi, India, 2, 827-830.
  • Gandomi, A.H., and A.H. Alavi (2012a), A new multi-gene genetic programming approach to nonlinear system modeling. Part I: materials and structural engineering problems, Neural Comput. Applic. 21, 1, 171-187, DOI: 10.1007/s00521-011-0734-z.
  • Gandomi, A.H., and A.H. Alavi (2012b), A new multi-gene genetic programming approach to nonlinear system modeling. Part II: geotechnical and earthquake engineering problems, Neural Comput. Applic. 21, 1, 189-201, DOI: 10.1007/s00521-011-0735-y.
  • Giustolisi, O., A. Doglioni, D.A. Savic, and B.W. Webb (2007), A multi-model approach to analysis of environmental phenomena, Environ. Modell. Softw. 22, 5, 674-682, DOI: 10.1016/j.envsoft.2005.12.026.
  • Goh, A.T.C. (1994), Seismic liquefaction potential assessed by neural networks, J. Geotech. Eng. ASCE 120, 9, 1467-1480, DOI: 10.1061/(ASCE)0733-9410 (1994)120:9(1467).
  • Goh, A.T.C., and S.H. Goh (2007), Support vector machines: Their use in geotechnical engineering as illustrated using seismic liquefaction data, Comput. Geotech. 34, 5, 410-421, DOI: 10.1016/j.compgeo.2007.06.001.
  • Hanna, A.M., D. Ural, and G. Saygili (2007), Neural network model for liquefaction potential in soil deposits using Turkey and Taiwan earthquake data, Soil Dyn. Earthq. Eng. 27, 6, 521-540, DOI: 10.1016/j.soildyn.2006.11.001.
  • Hwang, J.H., and C.W. Yang (2001), Verification of critical cyclic strength curve by Taiwan Chi-Chi earthquake data, Soil Dyn. Earthq. Eng. 21, 3, 237-257, DOI: 10.1016/S0267-7261(01)00002-1.
  • Idriss, I.M., and R.W. Boulanger (2010), SPT-based liquefaction triggering procedures, Report No. UCD/CGM-10/02, Department of Civil and Environmental Engineering, College of Engineering, University of California, Davis.
  • Javadi, A.A., M. Rezania, and M.M. Nezhad (2006), Evaluation of liquefaction induced lateral displacements using genetic programming, Comput. Geotech. 33, 4-5, 222-233, DOI: 10.1016/j.compgeo.2006.05.001.
  • Johari, A., G. Habibagahi, and A. Ghahramani (2006), Prediction of soil-water characteristic curve using genetic programming, J. Geotech. Geoenviron. Eng. 132, 5, 661-665, DOI: 10.1061/(ASCE)1090-0241(2006)132:5(661).
  • Juang, C.H., C.J. Chen, T. Jiang, and R.D. Andrus (2000), Risk-based liquefaction potential evaluation using standard penetration tests, Can. Geotech. J. 37, 6, 1195-1208, DOI: 10.1139/t00-064.
  • Koza, J.R. (1992), Genetic Programming: On the Programming of Computers by Means of Natural Selection, The MIT Press, Cambridge, MA.
  • Krammer, S.L. (1996), Geotechnical Earthquake Engineering, Pearson Education, India.
  • Ku, C.-S., D.-H. Lee, and J.-H. Wu (2004), Evaluation of soil liquefaction in the Chi-Chi, Taiwan earthquake using CPT, Soil Dyn. Earthq. Eng. 24, 9-10, 659-673, DOI: 10.1016/j.soildyn.2004.06.009.
  • Liao, S.S.C., D. Veneziano, and R.V. Whitman (1988), Regression models for evaluating liquefaction probability, J. Geotech. Eng. ASCE 114, 4, 389-411, DOI: 10.1061/(ASCE)0733-9410(1988)114:4(389).
  • MathWorks Inc. (2005), MatLab User’s Manual, Version 6.5, The MathWorks Inc., Natick.
  • Pal, M. (2006), Support vector machines-based modelling of seismic liquefaction potential, Int. J. Numer. An. Met. Geomech. 30, 10, 983-996, DOI: 10.1002/nag.509.
  • Rezania, M., and A.A. Javadi (2007), A new genetic programming model for predicting settlement of shallow foundations, Can. Geotech. J. 44, 12, 1462-1473, DOI: 10.1139/T07-063.
  • Samui, P. (2007), Seismic liquefaction potential assessment by using Relevance Vector Machine, Earthq. Eng. Eng. Vib. 6, 4, 331-336, DOI: 10.1007/s11803-007-0766-7.
  • Samui, P., and T.G. Sitharam (2011), Machine learning modelling for predicting soil liquefaction susceptibility, Nat. Hazard. Earth Sys. Sci. 11, 1-9, DOI: 10.5194/ nhess-11-1-2011.
  • Searson, D.P., D.E. Leahy, and M.J. Willis (2010), GPTIPS: an open source genetic programming toolbox from multigene symbolic regression. In: Proc. Int. Multi Conf. of Engineers and Computer Scientists, 17-19 March 2010, Hong Kong.
  • Seed, H.B., and I.M. Idriss (1971), Simplified procedure for evaluating soil liquefaction potential, J. Soil Mech. Found. Div. 97, 9, 1249-1273.
  • Yang, C.X., L.G. Tham, X.T. Feng, Y.J. Wang, and P.K.K. Lee (2004), Twostepped evolutionary algorithm and its application to stability analysis of slopes, J. Comput. Civil. Eng. 18, 145-153, DOI: 10.1061/(ASCE)0887-3801(2004)18:2(145).
  • Youd, T.L., I.M. Idriss, R.D. Andrus, I. Arango, G. Castro, J.T. Christian, R. Dobry, W.D.L. Finn, L.F. Harder, Jr., M.E. Hynes, K. Ishihara, J.P. Koester, S.S.C. Liao, W.F. Marcuson III, G.R. Martin, J.K. Mitchell, Y. Moriwaki,
  • M.S. Power, P.K. Robertson, R.B. Seed, and K.H. Stokoe II (2001), Liquefaction resistance of soils: Summary report from the 1996 NCEER and 1998 NCEER/NSF workshops on evaluation of liquefaction resistance of soils, J. Geotech. Geoenviron. Eng. 127, 10, 817-833, DOI: 10.1061/(ASCE)1090-0241(2001)127:10(817).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-b1d917bc-2259-4e57-8f8d-12220a0940dd
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