Advertisement

Natural Hazards

, Volume 71, Issue 3, pp 2101–2112 | Cite as

Seismic liquefaction potential assessed by fuzzy comprehensive evaluation method

  • Xinhua Xue
  • Xingguo Yang
Original Paper

Abstract

Liquefaction of loose, saturated granular soils during earthquakes poses a major hazard in many regions of the world. The determination of liquefaction potential of soils induced by earthquake is a major concern and an essential criterion in the design process of the civil engineering structures. A large number of factors that affect the occurrence of liquefaction during earthquake exist a form of uncertainty of non-statistical nature. Fuzzy systems are used to handle uncertainty from the data that cannot be handled by classical methods. It uses the fuzzy set to represent a suitable mathematical tool for modeling of imprecision and vagueness. The pattern classification of fuzzy classifiers provides a means to extract fuzzy rules for information mining that leads to comprehensible method for knowledge extraction from various information sources. Therefore, it is necessary to handle the soil liquefaction problem in a rational framework of fuzzy set theory. This study investigates the feasibility of using fuzzy comprehensive evaluation model for predicting soil liquefaction during earthquake. In the fuzzy comprehensive evaluation model of soil liquefaction, the following factors, such as earthquake intensity, standard penetration number, mean diameter and groundwater table, are selected as the evaluating indices. The results show that the method is a useful tool to assess the potential of soil liquefaction.

Keywords

Earthquake Soil liquefaction Fuzzy comprehensive evaluation Weight 

References

  1. Ben XD, Guo YH, Xie YW et al (2006) Application and discussion of fuzzy comprehensive evaluation in identifying mine inrush water source. Min Saf Environ Prot 33:57–59 (in Chinese)Google Scholar
  2. Boulanger RW, Mejia LH, Idriss IM (1997) Liquefaction at moss landing during Loma Prieta earthquake. J Geotech Geoenviron Eng ASCE 123(5):453–467CrossRefGoogle Scholar
  3. Castro G (1987) On the behavior of soils during earthquake-liquefaction. In: Cakmak AS (ed) Reprinted from soil dynamics and liquefaction. Princeton University, Princeton, NJGoogle Scholar
  4. Cetin KO et al (2004) Standard penetration test-based probabilistic and deterministic assessment of seismic soil liquefaction potential. J Geotech Geoenviron Eng ASCE 130(12):1314–1340CrossRefGoogle Scholar
  5. Goh ATC (1995) Seismic liquefaction potential assessed by neural networks. J Geotech Geoenviron Eng ASCE 120(9):1467–1480CrossRefGoogle Scholar
  6. Goh ATC, Goh SH (2007) Support vector machines: their use in geotechnical engineering as illustrated using seismic liquefaction data. Comput Geotech 34:410–421CrossRefGoogle Scholar
  7. Hanna AM, Ural DN, Saygili G (2007) Evaluation of liquefaction potential of soil deposits using artificial neural networks. Eng Comput 24(1):5–16CrossRefGoogle Scholar
  8. Hashash YMA (2007) Special issue on biologically inspired and other novel computing techniques in geomechanics. Comput Geotech 34(5):329–422CrossRefGoogle Scholar
  9. Juang CH, Yuan H, Lee DH et al (2003) Simplified cone penetration test-based method for evaluating liquefaction resistance of soils. J Geotech Geoenviron Eng ASCE 129(11):66–80CrossRefGoogle Scholar
  10. Kayadelen C (2011) Soil liquefaction modeling by genetic expression programming and neuro-fuzzy. Expert Syst Appl 38:4080–4087CrossRefGoogle Scholar
  11. Law KT, Cao YL, He GN (1990) An energy approach for assessing seismic liquefaction potential. Can Geotech J 27:320–329Google Scholar
  12. Liao SC, Veneziano D, Whitman RV (1988) Regression models for evaluating liquefaction probability. J Geotech Eng ASCE 114(4):389–411CrossRefGoogle Scholar
  13. Liu YP, Chen YM, Li YC et al (2009) Determining the development potential of reservoirs at ultra-high water cut stage using comprehensive fuzzy analytical hierarchy evaluation method. Syst Eng Theory Pract 29:181–185 (in Chinese)Google Scholar
  14. Ma YJ, Zheng XL, Li YX et al (2009) Improvement and application of fuzzy synthetic evaluation of groundwater quality. J China Univ Min Technol 38:745–750 (in Chinese)Google Scholar
  15. Ma L, Liu Y, Zhou XP (2010) Fuzzy comprehensive evaluation method of F statistics weighting in identifying mine water inrush source. Int J Eng Sci Technol 2(7):123–128Google Scholar
  16. Ministry of Construction of the People’s Republic of China (2002) Code for seismic design of buildings (in Chinese)Google Scholar
  17. Mohammad R, Akbar AJ, Orazio G (2010) Evaluation of liquefaction potential based on CPT results using evolutionary polynomial regression. Comput Geotech 37:82–92CrossRefGoogle Scholar
  18. Oommen T, Baise LG (2010) Model development and validation for intelligent data collection for lateral spread displacements. J Comput Civ Eng 24(6):467–477CrossRefGoogle Scholar
  19. Pal M (2006) Support vector machines-based modelling of seismic liquefaction potential. Int J Numer Anal Methods Geomech 30(10):966–983Google Scholar
  20. Ren WJ (2002) Application of artificial neural network in estimation and grade evaluation of foundation soil liquefaction. Hebei University of Technology, Tianjin (in Chinese)Google Scholar
  21. Robertson PK, Wride CE (1998) Evaluating cyclic liquefaction potential using the cone penetration test. Can Geotech J 35(3):442–459Google Scholar
  22. Samui P, Sitharam TC, Kurup PU (2008) OCR prediction using support vector machine based on piezocone data. J Geotech Geoenviron Eng 134(6):895–898CrossRefGoogle Scholar
  23. Seed HB, Idriss IM (1971) Simplified procedure for evaluating soil liquefaction potential. J Soil Mech Found Div ASCE 97(9):1249–1273Google Scholar
  24. Seed HB, Idriss IM, Arrango I (1983) Evaluation of liquefaction potential using field data. J Geotech Eng ASCE 109:458–484CrossRefGoogle Scholar
  25. Shuh GC, Ching YL, Chin CW (2008) CPT-based liquefaction assessment by using fuzzy–neural network. J Mar Sci Technol Taiwan 16(2):139–148Google Scholar
  26. Sladen JA, D’ Hollander RD, Krahn J (1985) The liquefaction of sands, a collapse surface approach. Can Geotech J 22(4):564–578Google Scholar
  27. Thomas O, Laurie GB, Richard V (2010) Validation and application of empirical liquefaction models. J Geotech Geoenviron Eng 136(12):1618–1633CrossRefGoogle Scholar
  28. Xue XH, Yang XG (2013) Application of the adaptive neuro-fuzzy inference system for prediction of soil liquefaction. Nat Hazards 67(2):901–917CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.State Key Laboratory of Hydraulics and Mountain River EngineeringSichuan UniversityChengduPeople’s Republic of China
  2. 2.College of Water Resource and HydropowerSichuan UniversityChengduPeople’s Republic of China

Personalised recommendations