Predicting lateral displacement caused by seismic liquefaction and performing parametric sensitivity analysis: Considering cumulative absolute velocity and fine content

Abstract

Lateral displacement due to liquefaction (D_H) is the most destructive effect of earthquakes in saturated loose or semi-loose sandy soil. Among all earthquake parameters, the standardized cumulative absolute velocity (CAV₅) exhibits the largest correlation with increasing pore water pressure and liquefaction. Furthermore, the complex effect of fine content (FC) at different values has been studied and demonstrated. Nevertheless, these two contexts have not been entered into empirical and semi-empirical models to predict D_H. This study bridges this gap by adding CAV₅ to the data set and developing two artificial neural network (ANN) models. The first model is based on the entire range of the parameters, whereas the second model is based on the samples with FC values that are less than the 28% critical value. The results demonstrate the higher accuracy of the second model that is developed even with less data. Additionally, according to the uncertainties in the geotechnical and earthquake parameters, sensitivity analysis was performed via Monte Carlo simulation (MCS) using the second developed ANN model that exhibited higher accuracy. The results demonstrated the significant influence of the uncertainties of earthquake parameters on predicting D_H.

Graphical abstract

Keywords

lateral spreading displacement / cumulative absolute velocity / fine content / artificial neural network / sensitivity analysis / Monte Carlo simulation

References

[1] Bhattacharya S, Hyodo M, Goda K, Tazoh T, Taylor C A. Liquefaction of soil in the Tokyo Bay area from the 2011 Tohoku (Japan) earthquake. Soil Dynamics and Earthquake Engineering, 2011, 31(11): 1618–1628

[2] Zhang Y, Dong S, Hou C, Guo C,Yao X, Li B, Du J, Zhang J.Geohazards induced by the Lushan Ms7.0 Earthquake in Sichuan Province, Southwest China: Typical examples, types and distributional characteristics. Acta Geologica Sinica (English Edition), 2013, 87(3): 646–657

[3] Franke K W. Development of a Performance-Based Model for Prediction of Lateral Spreading Displacements. Seattle: University of Washingtone, 2005

[4] Hamada M, Towhata I, Yasuda S, Isoyama R. Study on permanent ground displacement induced by seismic liquefaction. Computers and Geotechnics, 1987, 4(4): 197–220

[5] Shamoto Y, Zhang J M, Tokimatsu K. Methods for evaluating residual post-liquefaction ground settlement and horizontal displacement. Soils and Foundations, 1998, 38(Special): 69–83

[6] Kanıbir A, Ulusay R, Aydan Ö. Assessment of liquefaction and lateral spreading on the shore of Lake Sapanca during the Kocaeli (Turkey) earthquake. Engineering Geology, 2006, 83(4): 307–331

[7] Franke K W, Kramer S L. Procedure for the empirical evaluation of lateral spread displacement hazard curves. Journal of Geotechnical and Geoenvironmental Engineering, 2014, 140(1): 110–120

[8] Gillins D, Bartlett S. Multilinear regression equations for predicting lateral spread displacement from soil type and cone penetration test data. Journal of Geotechnical and Geoenvironmental Engineering, 2014, 140(4): 04013047

[9] Rezania M, Faramarzi A, Javadi A A. An evolutionary based approach for assessment of earthquake-induced soil liquefaction and lateral displacement. Engineering Applications of Artificial Intelligence, 2011, 24(1): 142–153

[10] Idriss I M, Boulange R W. Soil Liquefaction during Earthquakes. Monograph MNO-12. Oakland, CA: Earthquake Engineering Research Institute, 2008

[11] Zhang G, Robertson P K, Brachman R W I. Estimating liquefaction-induced lateral displacements using the standard penetration test or cone penetration test. Journal of Geotechnical and Geoenvironmental Engineering, 2004, 130(8): 861–871

[12] Faris A T, Seed R B, Kayen R E, Wu J. A semi-empirical model for the estimation of maximum horizontal displacement due to liquefaction-induced lateral spreading. In: The 8th U.S. National Conference of Earthquake Engineering. San Francisco, CA: Earthquake Engineering Research Institute, 2006

[13] Bray D J, Asce F, Travasarou T. Simplified procedure for estimating earthquake-induced deviatoric slope displacements. Journal of Geotechnical and Geoenvironmental Engineering, 2007, 133(4): 381–392

[14] Byrne P M, Seid-Karbasi M. Seismic liquefaction, lateral spreading, and flow slides: A numerical investigation into void redistribution.Canadian Geotechnical Journal, 2007, 44: 873–890

[15] Olson S M, Johnson C I. Analyzing liquefaction-induced lateral spreads using strength ratios. Journal of Geotechnical and Geoenvironmental Engineering, 2008, 134(8): 1035–1049

[16] Saygili G, Rathje E. Empirical predictive models for earthquake-induced sliding displacements of slopes. Journal of Geotechnical and Geoenvironmental Engineering, 2008, 134: 790–803

[17] Lam I, Arduino P, Mackenzie-Helnwein P. OPENSEES Soil-Pile Interaction Study under Lateral Spread Loading. Orlando, FL, 2009

[18] Arulanandan K, Li X S, Sivathasan K S. Numerical simulation of liquefaction-induced deformations. Journal of Geotechnical and Geoenvironmental Engineering, 2000, 126(7): 657–666

[19] Lopez-Caballero F, Modaressi Farahmand-Razavi A. Numerical simulation of liquefaction effects on seismic SSI. Soil Dynamics and Earthquake Engineering, 2008, 28(2): 85–98

[20] Tao M. Case History Verification of the Energy Method to Determine the Liquefaction Potential of Soil Deposits. Cleveland, OH: Case Western Reserve University, 2003, 173

[21] Derakhshandi M, Rathje E M, Hazirbaba K, Mirhosseini S M. The effect of plastic fines on the pore pressure generation characteristics of saturated sands. Soil Dynamics and Earthquake Engineering, 2008, 28(5): 376–386

[22] Phan V T A, Hsiao D H, Nguyen P T L. Effects of fines contents on engineering properties of sand-fines mixtures. Procedia Engineering, 2016, 142: 213–220

[23] Maurer B W, Green R A, Cubrinovski M, Bradley B A. Fines-content effects on liquefaction hazard evaluation for infrastructure in Christchurch, New Zealand. Soil Dynamics and Earthquake Engineering, 2015, 76: 58–68

[24] Pirhadi N, Tang X, Yang Q, Kang F. A new equation to evaluate liquefaction triggering using the response surface method and parametric sensitivity analysis. Sustainability, 2018, 11(1): 112–136

[25] Wang J, Rahman M S. A neural network model for liquefaction-induced horizontal ground displacement. Soil Dynamics and Earthquake Engineering, 1999, 18(8): 555–568

[26] Baziar M H, Ghorbani A. Evaluation of lateral spreading using artificial neural networks. Soil Dynamics and Earthquake Engineering, 2005, 25(1): 1–9

[27] Javadi A A, Rezania M, Nezhad M M. Evaluation of liquefaction induced lateral displacements using genetic programming. Computers and Geotechnics, 2006, 33(4–5): 222–233

[28] García S R, Romo M P, Botero E. A neurofuzzy system to analyze liquefaction-induced lateral spread. Soil Dynamics and Earthquake Engineering, 2008, 28(3): 169–180

[29] Hassan B M, Alireza S A. Evaluation of lateral spreading utilizing artificial neural network and genetic programming. International Journal of Civil Engineering, 2013, 11: 100–111

[30] Bartlett S F, Youd T L. Empirical Analysis of Horizontal Ground Displacement Generated by Liquefaction-Induced Lateral Spread. Tech. Rep. No. NCEER-92–0021. Buffalo, NY: National Center for Earthquake Engineering Research, 1992

[31] Youd T L, Hansen C M, Bartlett S F. Revised multilinear regression equations for prediction of lateral spread displacement. Journal of Geotechnical and Geoenvironmental Engineering, 2002, 128(12): 1007–1017

[32] Kramer S L, Mitchell R A. Ground motion intensity measures for liquefaction hazard evaluation. Earthquake Spectra, 2006, 22(2): 413–438

[33] Ganji H T, Alembagheri M, Khaneghahi M H. Evaluation of seismic reliability of gravity dam-reservoir in homogeneous foundation coupled system. Frontiers of Structural and Civil Engineering, 2019, 13(3): 701–715

[34] Haykin S. Neural Networks: A Comprehensive Foundation. 2nd ed.Prentice Hall, 1998

[35] Hornik K, Stinchcombe M, White H. Multilayer feed forward networks are universal approximators. Neural Networks, 1989, 2(5): 359–366

[36] Levenberg K. A method for the solution of certain non-linear problems in least squares. Quarterly of Applied Mathematics, 1944, 2(2): 164–168

[37] Rezaei S, Choobbasti A J. Liquefaction assessment using microtremor measurement, conventional method and artificial neural network (Case study: Babol, Iran). Frontiers of Structural and Civil Engineering, 2014, 8(3): 292–307

[38] Singh T, Pal M, Arora V K. Modeling oblique load carrying capacity of batter pile groups using neural network, random forest regression and M5 model tree. Frontiers of Structural and Civil Engineering, 2019, 13(3): 674–685

[39] Metropolis N, Ulam S. The Monte Carlo Method. Journal of the American Statistical Association, 1949, 44(247): 335–341

[40] Harr M E. Reliability-Based Design in Civil Engineering. New York: McGraw-Hill, 1987

[41] Levy S, Steinberg D M. Computer experiments: A review. Advances in Statistical Analysis, 2010, 94(4): 311–324

[42] EPRI. A Criterion for Determining Exceedance of the Operating Basis Earthquake. Report No. EPRI NP-5930. Palo Alto, CA, 1988

[43] EPRI. Standardization of the Cumulative Absolute Velocity. EPRI TR-100082–T2. Palo Alto, CA, 1991

[44] Luco N, Cornell C A. Structure-specific scalar intensity measures for near-source and ordinary earthquake ground motions. Earthquake Spectra, 2007, 23(2): 357–392

[45] Bartlett S F, Youd T L. Empirical prediction of liquefaction-induced lateral spread. Journal of Geotechnical Engineering, 1995, 121(4): 316–329

[46] Baziar M H, Nilipour N. Evaluation of liquefaction potential using neural-networks and CPT results. Soil Dynamics and Earthquake Engineering, 2003, 23(7): 631–636

[47] Hanna A M, Ural D, Saygili G. Neural network model for liquefaction potential in soil deposits using Turkey and Taiwan, China earthquake data. Soil Dynamics and Earthquake Engineering, 2007, 27(6): 521–540

[48] Hamdia K, Ghasemi H, Bazi Y, AlHichri H, Alajlan N, Rabczuk T. A novel deep learning based method for the computational material design of flexoelectric nanostructures with topology optimization. Finite Elements in Analysis and Design, 2019, 165: 21–30

[49] MAA. Soil Liquefaction Assessment and Remediation Study, Phase I (Yuanlin, Dachun, and Shetou), Summary Report and Appendixes. Taipei, Taiwan, China: Moh and Associates (MAA), Inc., 2000 (in Chinese)

[50] MAA. Soil Liquefaction Investigation in Nantou and Wufeng Areas. Taipei, Taiwan, China: Moh and Associates (MAA), Inc., 2000

[51] Chu D B, Stewart J P, Youd T L, Chu B L. Liquefaction-induced lateral spreading in near-fault regions during the 1999 Chi-Chi, Taiwan, China Earthquake. Journal of Geotechnical and Geoenvironmental Engineering, 2006, 132(12): 1549–1565

[52] Guo H, Zhuang X, Rabczuk T. A Deep collocation method for the bending analysis of Kirchhoff plate. Computers, Materials & Continua, 2019, 59(2): 433–456

[53] Anitescu C, Atroshchenko E, Alajlan N, Rabczuk T. Artificial neural network methods for the solution of second order boundary value problems. Computers, Materials & Continua, 2019, 59(1): 345–359

[54] Hamdia K M, Silani M, Zhuang X, He P, Rabczuk T. Stochastic analysis of the fracture toughness of polymeric nanoparticle composites using polynomial chaos expansions. International Journal of Fracture, 2017, 206(2): 215–227

[55] Hamdia K M, Ghasemi H, Zhuang X, Alajlan N, Rabczuk T. Sensitivity and uncertainty analysis for flexoelectric nanostructures. Computer Methods in Applied Mechanics and Engineering, 2018, 337: 95–109

[56] Vu-Bac N, Rafiee R, Zhuang X, Lahmer T, Rabczuk T. Uncertainty quantification for multiscale modeling of polymer nanocomposites with correlated parameters. Composites. Part B, Engineering, 2015, 68: 446–464

[57] Vu-Bac N, Silani M, Lahmer T, Zhuang X, Rabczuk T. A unified framework for stochastic predictions of mechanical properties of polymeric nanocomposites. Computational Materials Science, 2015, 96: 520–535

[58] Vu-Bac N, Lahmer T, Zhuang X, Nguyen-Thoi T, Rabczuk T. A software framework for probabilistic sensitivity analysis for computationally expensive models. Advances in Engineering Software, 2016, 100(C): 19–31

[59] Lumb P. The variability of natural soils. Canadian Geotechnical Journal, 1966, 3(2): 74–97

[60] Tan C P, Donald I B, Melchers R E. Probabilistic Slope Stability Analysis-State of Play. In: Proceedings of the Conference on Probabilistic Methods in Geotechnical Engineering. Canberra: CRC Press 1993

[61] Juang C H, Rosowsky D V, Tang W H. Reliability-Based Method for Assessing Liquefaction Potential of Soils. Journal of Geotechnical and Geoenvironmental Engineering, 1999, 125(8): 684–689

RIGHTS & PERMISSIONS

Higher Education Press