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.
Similar content being viewed by others
References
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)
Boulanger RW, Mejia LH, Idriss IM (1997) Liquefaction at moss landing during Loma Prieta earthquake. J Geotech Geoenviron Eng ASCE 123(5):453–467
Castro G (1987) On the behavior of soils during earthquake-liquefaction. In: Cakmak AS (ed) Reprinted from soil dynamics and liquefaction. Princeton University, Princeton, NJ
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):1314–1340
Goh ATC (1995) Seismic liquefaction potential assessed by neural networks. J Geotech Geoenviron Eng ASCE 120(9):1467–1480
Goh ATC, Goh SH (2007) Support vector machines: their use in geotechnical engineering as illustrated using seismic liquefaction data. Comput Geotech 34:410–421
Hanna AM, Ural DN, Saygili G (2007) Evaluation of liquefaction potential of soil deposits using artificial neural networks. Eng Comput 24(1):5–16
Hashash YMA (2007) Special issue on biologically inspired and other novel computing techniques in geomechanics. Comput Geotech 34(5):329–422
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):66–80
Kayadelen C (2011) Soil liquefaction modeling by genetic expression programming and neuro-fuzzy. Expert Syst Appl 38:4080–4087
Law KT, Cao YL, He GN (1990) An energy approach for assessing seismic liquefaction potential. Can Geotech J 27:320–329
Liao SC, Veneziano D, Whitman RV (1988) Regression models for evaluating liquefaction probability. J Geotech Eng ASCE 114(4):389–411
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)
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)
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):123–128
Ministry of Construction of the People’s Republic of China (2002) Code for seismic design of buildings (in Chinese)
Mohammad R, Akbar AJ, Orazio G (2010) Evaluation of liquefaction potential based on CPT results using evolutionary polynomial regression. Comput Geotech 37:82–92
Oommen T, Baise LG (2010) Model development and validation for intelligent data collection for lateral spread displacements. J Comput Civ Eng 24(6):467–477
Pal M (2006) Support vector machines-based modelling of seismic liquefaction potential. Int J Numer Anal Methods Geomech 30(10):966–983
Ren WJ (2002) Application of artificial neural network in estimation and grade evaluation of foundation soil liquefaction. Hebei University of Technology, Tianjin (in Chinese)
Robertson PK, Wride CE (1998) Evaluating cyclic liquefaction potential using the cone penetration test. Can Geotech J 35(3):442–459
Samui P, Sitharam TC, Kurup PU (2008) OCR prediction using support vector machine based on piezocone data. J Geotech Geoenviron Eng 134(6):895–898
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, Arrango I (1983) Evaluation of liquefaction potential using field data. J Geotech Eng ASCE 109:458–484
Shuh GC, Ching YL, Chin CW (2008) CPT-based liquefaction assessment by using fuzzy–neural network. J Mar Sci Technol Taiwan 16(2):139–148
Sladen JA, D’ Hollander RD, Krahn J (1985) The liquefaction of sands, a collapse surface approach. Can Geotech J 22(4):564–578
Thomas O, Laurie GB, Richard V (2010) Validation and application of empirical liquefaction models. J Geotech Geoenviron Eng 136(12):1618–1633
Xue XH, Yang XG (2013) Application of the adaptive neuro-fuzzy inference system for prediction of soil liquefaction. Nat Hazards 67(2):901–917
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Xue, X., Yang, X. Seismic liquefaction potential assessed by fuzzy comprehensive evaluation method. Nat Hazards 71, 2101–2112 (2014). https://doi.org/10.1007/s11069-013-0997-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11069-013-0997-z