Abstract
Soil liquefaction is one of recognized nonlinear devastating types of ground failures associated with earthquakes. The analyses frameworks for this phenomenon have been addressed using different methods and correlated triggering factors in case histories. In the current paper, a hybrid model using imperialistic competitive metaheuristic algorithm (ICA) incorporated with multi-objective generalized feedforward neural network (MOGFFN) for the purpose of liquefaction potential analysis was assessed. The optimum hybrid ICA-MOGFFN model was applied on a diversified database of 296 compiled case histories comprising nine of the most significant effective parameters on liquefaction. The result of ICA-MOGFFN model demonstrated for 3.01%, 2.09% and 7.46% progress in the success rates for the safety factor, liquefaction occurrence and depth of liquefaction. Accordingly, the conducted precision–recall curves showed 5.08%, 1.73% and 3.92% improvement compared to MOGFFN. Further evaluations using different statistical metrics represented superior progress in performance of hybrid ICA-MOGFFN. The capability of the developed method then was approved from observed agreement with other accepted procedures. The results implied that the developed hybrid model was a flexible and accurate enough tool that can effectively be applied for the liquefaction potential analyses. Using sensitivity analyses, the most and least effective inputs on the predicted liquefaction parameters were identified.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Abbreviations
- ICA :
-
Imperialistic competitive metaheuristic algorithm
- MOGFFN :
-
Multi-objective generalized feedforward neural network
- LPA :
-
Liquefaction potential analysis
- SPT/CPT :
-
Standard/cone penetration tests
- MLPs :
-
Multilayer percepterons
- γ:
-
Unit weight
- Vs :
-
Shear wave velocity
- FC :
-
Fine content
- CSR :
-
Cyclic stress ratio
- CRR :
-
Cyclic resistance ratio
- a max :
-
Maximum acceleration at investigated site
- σ′ v :
-
Effective vertical stress
- r d :
-
Stress reduction factor
- N cou :
-
Number of countries
- N col :
-
Number of colonies
- N imp :
-
Number of imperialists
- TA/AF/J :
-
Training algorithm/activation function/number of neurons in hidden layers
- RMSE min :
-
Minimum root mean square error
References
Abbaszadeh Shahri A (2016) Assessment and prediction of liquefaction potential using different artificial neural network models - a case study. Geotech Geol Eng 34(3):807–815. https://doi.org/10.1007/s10706-016-0004-z
Abbaszadeh Shahri A, Behzadafshar K, Rajablou R (2013) Verification of a new method for evaluation of liquefaction potential analysis. Arab J Geosci 6(3):881–892. https://doi.org/10.1007/s12517-011-0348-x
Abbaszadeh Shahri A, Rajablou R, Ghaderi A (2012a) An improved method for seismic site characterization with emphasis on liquefaction phenomena. Open J Earthq Res 1(2):13–21. https://doi.org/10.4236/ojer.2012.12002
Abbaszadeh Shahri A, Esfandiyari B, Rajablou R (2012b) A proposed geotechnical-based method for evaluation of liquefaction potential analysis subjected to earthquake provocations (case study: Korzan earth dam, Hamedan province, Iran). Arab J Geosci 5:555–564. https://doi.org/10.1007/s12517-010-0199-x
Abbaszadeh Shahri A, Larsson S, Renkel C (2020) Artificial intelligence models to generate visualized bedrock level: a case study in Sweden. Model Earth Syst Environ 6:1509–1528. https://doi.org/10.1007/s40808-020-00767-0
Abbaszadeh Shahri A, Larsson S, Johansson F (2015) CPT-SPT correlations using artificial neural network approach: a case study in Sweden. Electron J Geotech Eng Vol. 20. Bund 28:13439–13460
Ahmad M, Tang XW, Qiu JN, Ahmad F (2019) Evaluating seismic soil liquefaction potential using Bayesian belief network and C4.5 decision tree approaches. Appl Sci 9(20):4226https://doi.org/10.3390/app9204226
Arulampalam G, Bouzerdoum A (2002) Expanding the structure of shunting inhibitory artificial neural network classifiers. IJCNN, IEEE. https://doi.org/10.1109/IJCNN.2002.1007601
Asheghi R, Abbaszadeh Shahri A, Khorsand Zak M (2019) Prediction of uniaxial compressive strength of different quarried rocks using metaheuristic algorithm. Arabian J Sci Eng. https://doi.org/10.1007/s13369-019-04046-8
Asheghi R, Hosseini SA, Saneie M, Abbaszadeh Shahri A (2020) Updating the neural network sediment load models using different sensitivity analysis methods- a regional application. J Hydroinf 22(3):562–577. https://doi.org/10.2166/hydro.2020.098
Atashpaz-Gargari E, Hashemzadeh F, Rajabioun R, Lucas C (2008) Colonial competitive algorithm, a novel approach for PID controller design in MIMO distillation column process. Int J Intell Comput Cybern 1(3):337–355. https://doi.org/10.1108/17563780810893446
Atashpaz Gargari E, Lucas C (2007) Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In: Proceedings of the IEEE Congr Evol Comput, pp 4661–4667.
Barbosa EBM, Senne ELF (2017) Improving the fine-tuning of metaheuristics: an approach combining design of experiments and racing algorithms. J Optim. https://doi.org/10.1155/2017/8042436
Bi C, Fu B, Chen J, Zhao Y, Yang L, Duan Y, Shi Y (2019) Machine learning based fast multi-layer liquefaction disaster assessment. World Wide Web 22:1935–1950. https://doi.org/10.1007/s11280-018-0632-8
Boulanger RW, Idriss IM (2014) CPT and SPT based liquefaction triggering procedures. Report No. UCD/CGM-14/0, Center for Geotechnical Modeling, Department of Civil and Environmental Engineering, University of California, Davis, CA.
Cabalar AF, Cevik A, Gokceoglu C (2012) Some applications of adaptive neuro-fuzzy inference system (ANFIS) in geotechnical engineering. Comput Geotech 40:14–33. https://doi.org/10.1016/j.compgeo.2011.09.008
Cabalar AF, Cevik A (2009) Genetic programming-based attenuation relationship: an application of recent earthquakes in turkey. Comput Geosci 35(9):1884–1896. https://doi.org/10.1016/j.cageo.2008.10.015
Cetin KO, Seed RB, Moss RES, Der Kiureghian AK, Tokimatsu K, Harder LF, Kayen RE (2000) Field performance case histories for SPT-based evaluation of soil liquefaction triggering hazard. Report No. UCB/GT-2000/09, Geotechnical Engineering, Department of Civil Engineering, University of California at Berkeley, CA
Cetin KO, Seed RB, Kayen RE, Moss RES, Bilge HT, Ilgac M, Chowdhury K (2018) Dataset on SPT-based seismic soil liquefaction. Data Brief 20:544–548. https://doi.org/10.1016/j.dib.2018.08.043
Davis RO, Berrill JB (1998) Site specific prediction of liquefaction. Geotechnique 48(2):289–293. https://doi.org/10.1680/geot.1998.48.2.289
Fawcett T (2006) An introduction to ROC analysis. Pattern Recognit Lett 27(8):861–874. https://doi.org/10.1016/j.patrec.2005.10.010
Hand DJ, Till RJ (2001) A simple generalization of the area under the ROC curve for multiple class classification problems. Mach Learn 45:171–186. https://doi.org/10.1023/A:1010920819831
Finn WD (2002) State of the art for the evaluation of seismic liquefaction potential. Comput Geotech 29(5):328–341. https://doi.org/10.1016/S0266-352X(01)00031-3
Furman GG (1965) Comparison of models for subtractive and shunting lateral-inhibition in receptor-neuron fields. Kybernetik 2:257–274. https://doi.org/10.1007/BF00274089
Ghaderi A, Abbaszadeh Shahri A, Larson S (2019) An artificial neural network based model to predict spatial soil type distribution using piezocone penetration test data (CPTu). Bull Eng Geol Env 78(6):4579–4588. https://doi.org/10.1007/s10064-018-1400-9
Goh AT (2002) Probabilistic neural network for evaluating seismic liquefaction potential. Can Geotech J 39(1):219–232. https://doi.org/10.1139/t01-073
Green RA, Cubrinovski M, Cox B, Wood C, Wotherspoon L, Bradley B, Maurer B (2014) Select liquefaction case histories from the 2010–2011 Canterbury earthquake sequence. Earthq Spectra 30(1):131–153. https://doi.org/10.1193/030713EQS066M
Hakam A (2016) Laboratory liquefaction test of sand based on grain size and relative density. J Eng Technol Sci 48(3):334–344. https://doi.org/10.5614/j.eng.technol.sci.2016.48.3.7
Hazen A (1919) Hydraulic-fill dams. Trans ASCE 83(1):1713–1745
Hernandez-Orallo J (2013) ROC curves for regression. Pattern Recogn 46(12):3395–3411. https://doi.org/10.1016/j.patcog.2013.06.014
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. https://doi.org/10.1007/s10064-016-0924-0
Holmes DS, Mergen AE (1995) An alternative method to test for randomness of a process. Qual Reliab Eng Int 11(3):171–174. https://doi.org/10.1002/qre.4680110306
Hosseini S, Al Khaled A (2014) A survey on the imperialist competitive clgorithm metaheuristic: implementation in engineering domain and directions for future research. Appl Soft Comput. https://doi.org/10.1016/j.asoc.2014.08.024
Ishibashi I (1985) Effect of grain characteristics on liquefaction potential- In search of standard sand for cyclic strength. Geotech Test J 8(3):137–139. https://doi.org/10.1520/GTJ10525J
Ji X, Gao Q, Yin F, Guo H (2016) An efficient imperialist competitive algorithm for solving the QFD decision problem. Math Probl Eng. https://doi.org/10.1155/2016/2601561
Joanes DN, Gill CA (1998) Comparing measures of sample skewness and kurtosis. J R Stat Soc Ser D 47(1):183–189. https://doi.org/10.1111/1467-9884.00122
Kaveh A, Hamze-Ziabari SM, Bakhshpoori T (2018) Patient rule-induction method for liquefaction potential assessment based on CPT data. Bull Eng Geol Environ 77(2):849–865. https://doi.org/10.1007/s10064-016-0990-3
Kayadelen C (2011) Soil liquefaction modeling by genetic expression programming and neuro-fuzzy. Expert Syst Appl 38(4):4080–4087. https://doi.org/10.1016/j.eswa.2010.09.071
Kramer SL, Seed HB (1988) Initiation of soil liquefaction under static loading conditions. J Geotech Eng 114(4):412–430. https://doi.org/10.1061/(ASCE)0733-9410(1988)114:4(412)
Kramer S (1996) Geotechnical earthquake engineering. Upper Saddle River, Prentice-Hall, NJ
Koch C, Poggio T, Torre V (1983) Nonlinear interactions in a dendritic tree: localization, timing, and role in information processing. Proc Natl Acad Sci USA 80:2799–2802
Kouzegar K (2013) Study of possibility of liquefaction in the body and foundation of embankment dams - case study of Sattarkhan Dam. World Appl Sci J 21(12):1795–1803. https://doi.org/10.5829/idosi.wasj.2013.21.12.2007
Krekelberg B (2008) Motion detection mechanisms. Sens Comprehens Ref 2:133–155. https://doi.org/10.1016/B978-012370880-9.00305-4
Liao SSC, Veneziano D, Whitman RV (1988) Regression models for evaluating liquefaction probability. J Geotech Eng ASCE 114(4):389–411
Liao SSC, Whitman RV (1986) Catalogue of liquefaction and non-liquefaction occurrences during earthquakes. Res. Rep., Dept. of Civ. Engng., Massachusetts Institute of Technology, Cambridge, Mass.
Lin JL, Tsai YH, Yu CY, Li MS (2012) Interaction enhanced imperialist competitive algorithms. Algorithms 5:433–448. https://doi.org/10.3390/a5040433
Naghizadehrokni M, Choobbasti AJ, Naghizadehrokni M (2018) Liquefaction maps in Babol city, Iran through probabilistic and deterministic approaches. Geoenviron Disast 5:2. https://doi.org/10.1186/s40677-017-0094-9
Njok PGA, Shen SL, Zhou A, Lyu HM (2020) Evaluation of soil liquefaction using AI technology incorporating a coupled ENN/ t-SNE model. Soil Dyn Earthq Eng 130:105988. https://doi.org/10.1016/j.soildyn.2019.105988
Pal M (2006) Support vector machines-based modelling of seismic liquefaction potential. Int J Numer Anal Methods Geomech 30:983–996. https://doi.org/10.1002/nag.509
Pan Z, Lei D, Zhang Q (2018) A new imperialist competitive algorithm for multiobjective low carbon parallel machines scheduling. Math Probl Eng. https://doi.org/10.1155/2018/5914360
Rahbarzadeh A, Azadi M (2019) Improving prediction of soil liquefaction using hybrid optimization algorithms and a fuzzy support vector machine. Bull Eng Geol Environ 78(7):4977–4987. https://doi.org/10.1007/s10064-018-01445-3
Rahman MS, Wang J (2002) Fuzzy neural network models for liquefaction prediction. J Soil Dyn Earthquake Eng 22:685–694
Ramakrishnan D, Singh TN, Purwar N, Barde 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. https://doi.org/10.1007/s10596-008-9088-8
Samui P, Kim D, Sitharam TG (2011) Support vector machine for evaluating seismic liquefaction potential using shear wave velocity. J Appl Geophys 73(1):8–15. https://doi.org/10.1016/j.jappgeo.2010.10.005
Samui P, Jagan J, Hariharan R (2016) An alternative method for determination of liquefaction susceptibility of soil. Geotech Geol Eng 34:735–738. https://doi.org/10.1007/s10706-015-9969-2
Sawicki A, Mierczynski J (2006) Developments in modeling liquefaction of granular soils caused by cyclic loads. Appl Mech Rev 59:91–106. https://doi.org/10.1115/1.2130362
Seed HB, Idriss IM (1971) Simplified procedure for evaluating soil liquefaction potential. J Soil Mech Found Div ASCE 97(9):1249–1273
Seed HB, Idriss IM, Arango I (1983) Evaluation of liquefaction potential using field performance data. J Geotech Eng Div ASCE 109(3):458–482. https://doi.org/10.1061/(ASCE)0733-9410(1983)109:3(458)
Tokimatsu K, Yoshimi Y (1983) Empirical correlation of soil liquefaction based on SPT N-value and fines content. Soils Found 23(4):56–74. https://doi.org/10.3208/sandf1972.23.4_56
Tung AT, Wang YY, Wong FS (1993) Assessment of liquefaction potential using neural networks. Soil Dyn Earthq Eng 12(6):325–335. https://doi.org/10.1016/0267-7261(93)90035-P
Vida I, Bartos M, Jonas P (2006) Shunting inhibition improves robustness of gamma oscillations in hippocampal interneuron networks by homogenizing firing rates. Neuron 49:107–117. https://doi.org/10.1016/j.neuron.2005.11.036
Von Neumann J, Bellinson HR, Hart BI (1941) The mean square successive difference. Ann Math Stat 12:153–162
Willmott CJ (1984) On the evaluation of model performance in physical geography. Spatial Stat Models 443–460.
**ng B, Gao WJ (2013) Imperialist competitive algorithm. In: Innovative computational intelligence: a rough guide to 134 clever algorithms. Intelligent systems reference library, 62, 203–209, Springer, Cham, https://doi.org/10.1007/978-3-319-03404-1_15.
Xue X, Yang X (2013) Application of the adaptive neuro-fuzzy inference system for prediction of soil liquefaction. Nat Hazards 67:901–917. https://doi.org/10.1007/s11069-013-0615-0
Xue X, Yang X (2016a) Seismic liquefaction potential assessed by support vector machines approaches. Bull Eng Geol Environ 75:153–162. https://doi.org/10.1007/s10064-015-0741-x
Xue X, **ao M (2016b) 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
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
Yegaian MK, Ghahraman VG, Nogole-Sadat MAA, Daraei H (1995) Liquefaction during the 1990 Manjil, Iran, Earthquake, I: case history data. Bull Seismol Soc Am 85(1):66–82
Youd TL, Idriss IM, Andrus RD, Arango I, Castro I, Christian JT, Dorby R, Finn WDLL, Harder F, Hynes ME, Ishihara K, Koester JP, Laio SC, Marcuson WF, Martin GR, Mitchell JK, MoriwakiY PMS, Robertson PK, Seed RB, Stokoe KH (2001) Liquefaction resistance of soils: summery report from the 1996 NCEER and 1998 NCEER/NSF workshop on evaluation of liquefaction resistance of soils. J Geotech Geoenviron Eng 127(10):817–833
Zhou J, Li E, Wang M, Chen X, Shi X, Jiang L (2019) Feasibility of stochastic gradient boosting approach for evaluating seismic liquefaction potential based on SPT and CPT case histories. J Perform Constr Facil 33(3):04019024. https://doi.org/10.1061/(ASCE)CF.1943-5509.0001292
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Abbaszadeh Shahri, A., Maghsoudi Moud, F. Liquefaction potential analysis using hybrid multi-objective intelligence model. Environ Earth Sci 79, 441 (2020). https://doi.org/10.1007/s12665-020-09173-2
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s12665-020-09173-2