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SPT-based liquefaction assessment with a novel ensemble model based on GMDH-type neural network

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Abstract

Liquefaction is one of the most complex problems in geotechnical earthquake engineering. This paper proposes a novel ensemble group method of data handling (EGMDH) model based on classification for the prediction of liquefaction potential of soils. The database used in this study consists of 451 standard penetration test (SPT)–based case records from two major earthquakes. The input parameters are selected as SPT blow numbers, percent finest content less than 75 μm, depth of groundwater table, total and effective overburden stresses, maximum peak ground acceleration, and magnitude of earthquake for the prediction models. The proposed EGMDH model results were also compared with other classifier models, particularly the results of the GMDH model. The results of this study indicated that the proposed EGMDH model has achieved more successful results on predicting the liquefaction potential of soils compared with the other classifier models by improving the prediction performance of GMDH model.

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References

  • Abdalla JA, Attom MF, Hawileh R (2015) Prediction of minimum factor of safety against slope failure in clayey soils using artificial neural network. Environ Earth Sci 73(9):5463–5477

    Article  Google Scholar 

  • Aggarwal M (2013) Performance analysis of different feature selection methods in intrusion detection. Int J Sci Technol Res 2(6):225–231

    Google Scholar 

  • Ali J, Khan R, Ahmad N, Maqsood I (2012) Random forests and decision trees. Int J Comput Sci Issues 9(5):272

    Google Scholar 

  • Andrus RD, Stokoe KH II (2000) Liquefaction resistance of soils from shear wave velocity. J Geotech Geoenviron Eng 126(11):1015–1025

    Article  Google Scholar 

  • Ardakani A, Kordnaeij A (2017) Soil compaction parameters prediction using GMDH-type neural network and genetic algorithm. Eur J Environ Civ Eng 23(4):449–462. https://doi.org/10.1080/19648189.2017.1304269

    Article  Google Scholar 

  • Augusty SM, Izudheen S (2013) Ensemble classifiers A survey: evaluation of ensemble classifiers and data level methods to deal with imbalanced data problem in protein- protein interactions. Rev Bionformatics Biometrics 2(1):1–9

    Google Scholar 

  • Baziar MH, Nilipour N (2003) Evaluation of liquefaction potential using neural-networks and CPT results. Soil Dyn Earthq Eng 23(7):631–636

    Article  Google Scholar 

  • Boulanger RW, Idriss IM (2012) Probabilistic standard penetration test–based liquefaction–triggering procedure. J Geotech Geoenviron Eng ASCE 138(10):1185–1195

    Article  Google Scholar 

  • Cetin KO, Seed RB, Der Kiureghian A, Tokimatsu K, Harder JLF, Kayen RE, Moss RES (2004) SPT-based probabilistic and deterministic assessment of seismic soil liquefaction potential. ASCE J Geotech Geoenvir Eng 130(12):1314–1340

    Article  Google Scholar 

  • Chenari RJ, Tizpa P, Rad MRG, Machado SL, Fard MK (2015) The use of index parameters to predict soil geotechnical properties. Arab J Geosci 8(7):4907–4919

    Article  Google Scholar 

  • Chern SG, Lee CY, Wang CC (2008) CPT-based liquefaction assessment by using fuzzy-neural network. J Mar Sci Technol 16(2):139–148

    Google Scholar 

  • Chik Z, Aljanabi QA, Kasa A, Taha MR (2014) Tenfold cross validation artificial neural network modeling of the settlement behavior of a stone column under a highway embankment. Arab J Geosci 7(11):4877–4887. https://doi.org/10.1007/s12517-013-1128-6

    Article  Google Scholar 

  • Coduto DP (2003) Geotechnical engineering, principles and practice. Prentice-Hall, New Delhi, pp 137–155

    Google Scholar 

  • Cortes C, Vapnik V (1995) Support vector networks. Mach Learn 20(3):273–297

    Google Scholar 

  • Das SK, Basudhar PK (2008) Prediction of residual friction angle of clays using artificial neural network. Eng Geol 100(3–4):142–145

    Article  Google Scholar 

  • Elgamal AW, Dobry R, Adalıer K (1989) Small-scale shaking table tests of saturated layered sand-silt deposits, 2nd U.S-Japan Workshop on soil liquefaction, Buffalo, N.Y., NCEER Rep. No. 890032, 233–245

  • Erzin Y, Ecemis N (2015) The use of neural networks for CPT-based liquefaction screening. Bull Eng Geol Environ 74(1):103–116

    Article  Google Scholar 

  • Ghanadzadeh H, Ganji M, Fallahi S (2012) Mathematical model of liquid–liquid equilibrium for a ternary system using the GMDH-type neural network and genetic algorithm. Appl Math Model 36:4096–4105

    Article  Google Scholar 

  • Goh ATC (1994) Seismic liquefaction potential assessed by neural networks. J Geotech Eng 120(9):1467–1480

    Article  Google Scholar 

  • Goh ATC (1996) Neural network modeling of CPT seismic liquefaction data. J Geotech Eng 122(1):70–73

    Article  Google Scholar 

  • Goh ATC (2002) Probabilistic neural network for evaluating seismic liquefaction potential. Can Geotech J 39:219–232

    Article  Google Scholar 

  • Goharzaya M, Noorzada A, Ardakania AM, Jalal M (2017) A worldwide SPT-based soil liquefaction triggering analysis utilizing gene expression programming and Bayesian probabilistic method. J Rock Mech Geotech Eng 9(4):683–693

    Article  Google Scholar 

  • Hanna AM, Ural D, Saygili G (2007) Neural network model for liquefaction potential in soil deposits using Turkey and Taiwan earthquake data. Soil Dyn Earthq Eng 27(6):521–540

    Article  Google Scholar 

  • Hassanlourad M, Ardakani A, Kordnaeij A, Mola-Abasi H (2017) Dry unit weight of compacted soils prediction using GMDH-type neural network. Eur Phys J Plus 132:357

    Article  Google Scholar 

  • Haykin S (1994) Neural network: a comprehensive foundation. MacMillan College Publishing Co, New York

    Google Scholar 

  • Hoang ND, Bui DT (2018) Predicting earthquake-induced soil liquefaction based on a hybridization of kernel Fisher discriminant analysis and a least squares support vector machine: a multi-dataset study. Bull Eng Geol Environ 77(1):191–204

    Article  Google Scholar 

  • Husmand B, Scott F, Crouse CB (1988) Centrifuge liquefaction tests in a laminar box. Geotechnique 38(2):253–262

    Article  Google Scholar 

  • Idriss IM, Boulanger RW (2006) Semi-empirical procedures for evaluating liquefaction potential during earthquakes. Int J Soil Dyn Earthquake Eng 26:115–130

    Article  Google Scholar 

  • Idriss IM, Boulanger RW (2010) SPT-based liquefaction triggering procedures. Rep. UCD/CGM-10/02, Dept. of Civil and Environmental Engineering, Univ. of California, Davis, CA

  • Ishihara K (1996) Soil behaviour in earthquake geotechnics. The Oxford Engineering Science Series, Oxford

    Google Scholar 

  • Ivakhnenko AG (1971) Polynomial theory of complex systems. IEEE Trans Syst Man Cybern A Syst Hum 1:364–378. https://doi.org/10.1109/TSMC.1971.4308320

    Article  Google Scholar 

  • Ivakhnenko AG (1976) The group method of data handling in prediction problems. Sov Autom Control Avtomotika 9:21–30

    Google Scholar 

  • Iwasaki T, Tokida K, Tatsuoka F (1981) Soil liquefaction potential evaluation with use of the simplified procedure. International Conference on Recent Advances in Geotechnical Earthquake Engineering and Soil Dynamics, St. Louis, pp 209–214

    Google Scholar 

  • Jirdehi RA, Mamoudan HT, Sarkaleh HH (2014) Applying GMDH-type neural network and particle warm optimization for prediction of liquefaction induced lateral displacements. Appl Appl Math Int J 9(2):528–540

    Google Scholar 

  • Juang CH, Chen CJ (1999) CPT-based liquefaction evaluation using artificial neural networks. Comput Aided Civil Infrastruct Eng 14(3):221–229

    Article  Google Scholar 

  • Kalinli A, Acar MC, Gunduz Z (2011) New approaches to determine the ultimate bearing capacity of shallow foundations based on artificial neural networks and ant colony optimization. Eng Geol 117(1–2):29–38. https://doi.org/10.1016/j.enggeo.2010.10.002

    Article  Google Scholar 

  • Karthikeyan J, Kim D, Aiyer BG, Samui P (2013) SPT-based liquefaction potential assessment by relevance vector machine approach. Eur J Environ Civ Eng 17(4):248–262. https://doi.org/10.1080/19648189.2013.781546

    Article  Google Scholar 

  • Kaya Y (2013) A new intelligent classifier for breast cancer diagnosis based on a rough set and extreme learning machine: RS+ ELM. Turk J Electr Eng Comput Sci 21(Sup. 1):2079–2091

    Article  Google Scholar 

  • Kiefa MAA (1998) General regression neural networks for driven piles in cohesionless soils. Geotech Geoenviron Eng 124(12):1177–1185

    Article  Google Scholar 

  • Kim YS, Kim BT (2006) Use of artificial neural networks in the prediction of liquefaction resistance of sands. J. Geotech. Geoenviron. Eng. ASCE 132(11):1502–1504. https://doi.org/10.1061/ASCE1090-02412006132:111502

    Article  Google Scholar 

  • Kondo T, Ueno J (2012) Feedback GMDH-type neural network and its application to medical image analysis of liver cancer. In 42th ISCIE international symposium on stochastic systems theory and its applications, pages 81–82

  • Kordnaeij A, Kalantary F, Kordtabar B, Mola-Abasi H (2015) Prediction of recompression index using GMDH-type neural network based on geotechnical soil properties. Soils Found 55(6):1335–1345

    Article  Google Scholar 

  • Kramer SL (1996) Geotechnical earthquake engineering. Prentice-Hall, Upper Saddle River, p 653

  • Kramer SL, Mayfield RT (2007) The return period of soil liquefaction. J Geotech Geoenviron Eng 133(7):802–813

    Article  Google Scholar 

  • Kuo YL, Jaksa MB, Lyamin AV, Kaggwa WS (2009) ANN-based model for predicting the bearing capacity of strip footing on multi-layered cohesive soil. Comput Geotech 36(3):503–516. https://doi.org/10.1016/j.compgeo.2008.07.002

    Article  Google Scholar 

  • Lambe PC (1981) Dynamic centrifuge modelling of a horizontal sand stratum, ScD Thesis, Dept. Of Civil Engineering, Massachusetts Institute of Technology, Cambridge, USA

  • Le Cessie S, Van Houwelingen JC (1992) Ridge estimators in logistic regression. J R Stat Soc: Ser C: Appl Stat 41(1):191–201

    Google Scholar 

  • Lee I, Lee J (1996) Prediction of pile bearing capacity using artificial neural networks. Comput Geotech 18(3):189–200. https://doi.org/10.1016/0266-352X(95)00027-8

    Article  Google Scholar 

  • Lee C, Lee GG (2006) Information gain and divergence-based feature selection for machine learning-based text categorization. Inf Process Manag 42(1):155–165

    Article  Google Scholar 

  • Liu H, Qiao T (1984) Liquefaction potential of saturated sand deposits underlying foundation of structure, Proceeding of 8th World Conference on Earthquake Engineering, San Francisco, 3, 199–206

  • Muduli PK, Das SK (2015a) Model uncertainty of SPT-based method for evaluation of seismic soil liquefaction potential using multi-gene genetic programming. Soils Found 55(2):258–275

    Article  Google Scholar 

  • Muduli PK, Das SK (2015b) First order reliability method for probabilistic evaluation of liquefaction potential of soil using genetic programming. Int J Geomech ASCE 15(3):04014052

    Article  Google Scholar 

  • Mughieda O, Bani HK, Safieh B (2009) Liquefaction assessment by artificial neural networks based on CPT. Int J Geotech Eng 2:289–302

    Article  Google Scholar 

  • Nejad FP, Jaksa MB, Kakhi M, McCabe BA (2009) Prediction of pile settlement using artificial neural networks based on standard penetration test data. Comput Geotech 36(7):1125–1133

    Article  Google Scholar 

  • Pal M, Mather PM (2003) An assessment of the effectiveness of decision tree methods for land cover classification. Remote Sens Environ 86:554–565

    Article  Google Scholar 

  • Rahman MS, Wang J (2002) Fuzzy neural network models for liquefaction prediction. Soil Dyn Earthq Eng 22:685–694

    Article  Google Scholar 

  • Ramakrishnan D, Singh TN, Purwar N, Badre KS, Gulati A, Gupta S (2008) Artificial neural network and liquefaction susceptibility assessment: a case study using the 2001 Bhuj earthquake data, Gujarat, India. Comput Geosci 12:491–501

    Article  Google Scholar 

  • Robertson PK, Wride CE (1998) Evaluating cyclic liquefaction potential using the cone penetration test. Can Geotech J 35(3):442–459

    Article  Google Scholar 

  • Sakellariou MG, Ferentinou M (2005) A study of slope stability prediction using neural networks. Geotech Geol Eng 24(3):419–445

    Article  Google Scholar 

  • Samui P, Sitharam TG (2011) Machine learning modelling for predicting soil liquefaction susceptibility. Nat Hazards Earth Syst Sci 11:1–9

    Article  Google Scholar 

  • Seed HB, Idriss IM (1971) Simplified procedure for evaluating soil liquefaction potential. Journal of Soil Mech Foundation Div ASCE 97(9):1249–1273

    Google Scholar 

  • Stokoe KH, Roesset JM, Bierschwale JG, Aouad M (1988) Liquefaction potential of sands from shear wave velocity. Proceedings of Ninth World Conference on Earthquake Engineering, Tokyo, Japan, 3, 213–218.

  • Sulewska MJ (2011) Applying artificial neural networks for analysis of geotechnical problems. Comput Assist Mech Eng Sci 18:231–241

    Google Scholar 

  • Suzuki Y, Koyamada K, Tokimatsu K (1997) Prediction of liquefaction resistance based on CPT tip resistance and sleeve friction. Proceedings XIV International Conference of Soil Mechanics and Foundation Engineering, Hamburg, Germany, 603–606

  • Tokimatsu K, Yoshimi Y (1983) Empirical correlation of soil liquefaction based on SPT N-value and fines content. Soils Found 23(4):56–74

    Article  Google Scholar 

  • Vissikirsky VA, Stepashko VS, Kalavrouziotis IK, Drakatos PA (2005) Growth dynamics of trees irrigated with wastewater: GMDH modeling, assessment, and control issues. Instrum Sci Technol 33(2):229–249

    Article  Google Scholar 

  • Wang HB, Xu WY, Xu RC (2005) Slope stability evaluation using back propagation neural networks. Eng Geol 80:302–315

    Article  Google Scholar 

  • Xue X, Liu E (2017) Seismic liquefaction potential assessed by neural networks. Environ Earth Sci 76:192. https://doi.org/10.1007/s12665-017-6523-y

    Article  Google Scholar 

  • Xue X, Xiao M (2016) Application of genetic algorithm-based support vector machines for prediction of soil liquefaction. Environ Earth Sci 75:874. https://doi.org/10.1007/s12665-016-5673-7

    Article  Google Scholar 

  • Youd TL, Perkins DM (1978) Mapping liquefaction- induced ground failure potential. J Geotech Eng Div 104(4):443–446

    Google Scholar 

  • Youd TL, Idriss IM, Andrus RD, Arango I, Castro G, Christian JT, Dobry R, Liam Finn WD, Harder LF Jr, Hynes ME, Ishihara K, Koester JP, Laio SSC, Marcuson WF III, Martin GR, Mitchell JK, Moriwaki Y, Power MS, Robertson PK, Seed RB, Stokoe KH (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

    Article  Google Scholar 

  • Zhu W, Wang J, Zhang W, Sun D (2012) Short-term effects of air pollution on lower respiratory diseases and forecasting by the group method of data handling. Atmos Environ 51:29–38

    Article  Google Scholar 

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Authors and Affiliations

  1. Vocational School of Technical Sciences, Department of Transportation Services, Mersin University, Mersin, Turkey

    Talas Fikret Kurnaz

  2. Department of Computer Engineering, Faculty of Engineering and Architecture, Siirt University, Siirt, Turkey

    Yilmaz Kaya

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  1. Talas Fikret Kurnaz
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  2. Yilmaz Kaya
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Correspondence to Talas Fikret Kurnaz.

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Kurnaz, T.F., Kaya, Y. SPT-based liquefaction assessment with a novel ensemble model based on GMDH-type neural network. Arab J Geosci 12, 456 (2019). https://doi.org/10.1007/s12517-019-4640-5

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