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

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Abstract

Lateral displacement due to liquefaction (DH) 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 DH 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 DH.

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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

    Article  Google Scholar 

  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

    Article  Google Scholar 

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

    Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

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

    Google Scholar 

  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

    Article  Google Scholar 

  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

    Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Google Scholar 

  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

    Google Scholar 

  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

    Article  Google Scholar 

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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  MATH  Google Scholar 

  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

    Article  MathSciNet  MATH  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

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

    Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  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

    Article  Google Scholar 

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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  MathSciNet  Google Scholar 

  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)

    Google Scholar 

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

    Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  MathSciNet  MATH  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

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

    Article  Google Scholar 

  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

    Google Scholar 

  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

    Article  Google Scholar 

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Acknowledgements

The authors are grateful for the technical and financial support provided by the Scientific Innovation Group for Youths of Sichuan Province (No. 2019JDTD0017).

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Authors and Affiliations

  1. School of Civil Engineering and Geomatics, Southwest Petroleum University, Chengdu, 610500, China

    Nima Pirhadi & Hazem Samih Mohamed

  2. State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian, 116024, China

    Xiaowei Tang & Qing Yang

  3. Civil Engineering Discipline, Department of Engineering, International College of Auckland, Auckland, 1010, New Zealand

    Afshin Asadi

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  1. Nima Pirhadi
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  2. Xiaowei Tang
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  5. Hazem Samih Mohamed
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Correspondence to Nima Pirhadi.

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Pirhadi, N., Tang, X., Yang, Q. et al. Predicting lateral displacement caused by seismic liquefaction and performing parametric sensitivity analysis: Considering cumulative absolute velocity and fine content. Front. Struct. Civ. Eng. 15, 506–519 (2021). https://doi.org/10.1007/s11709-021-0677-0

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