Adopting a Fuzzy C-Means Clustering Method to Evaluate Soil Liquefaction from SPT Measurements
DOI:
https://doi.org/10.65904/3083-3590.2026.02.11Keywords:
Soil liquefaction, Earthquake hazard, Fuzzy C-means clustering, Reliability, Liquefaction possibility, Computer coding, Data miningAbstract
Soil liquefaction occurs during major earthquakes in saturated, relatively shallow soils and is the primary cause of building foundation failures. Hence, assessing the likelihood of soil liquefaction has become one of the most important areas of study in geotechnical and earthquake engineering. However, the complex nonlinear behavior of liquefiable soils and the nondeterministic nature of earthquakes make liquefaction analysis difficult and uncertain. In the present study, we address this uncertainty by applying fuzzy set theory and adapting the SPT-based cyclic stress approach to the Fuzzy C-means Clustering Method (FCM). In this context, several liquefaction records are compiled from the cited literature and used to assess the possibility of liquefaction occurrence. The effectiveness of this proposed method in handling the vagueness of liquefaction occurrence is compared with the conventional deterministic method and a probabilistic method of liquefaction assessment. The results show the new method’s clear advantages over both the deterministic and probabilistic methods, particularly as the fine content of the liquefiable soil increases.
References
[1] Seed HB, Idriss IM. Simplified procedure for evaluating soil liquefaction potential. J Soil Mech Found Div 1971; 97(9): 1249-73.
https://doi.org/10.1061/JSFEAQ.0001662
[2] Youd TL, Idriss IM, Andrus RD, Arango I, Castro G, Christian JT, Dobry R, Finn WDL, Harder LF, Hynes ME, Ishihara K, Koester JP, Liao ASC, Marcuson WF, Martin R, Mitchell JK, Moriwaki Y, Power MS, Robertson PK, Seed RB, Stokoe KH. Liquefaction resistance of soils; summary report from the 1996 NCEER and 1998 NCEER/NSF workshops on evaluation of liquefaction resistance of soils. J Geotech Geoenviron Eng 2001; 127(10): 817-33. https://doi.org/10.1061/(ASCE)1090-0241(2001)127:10(817)
[3] Tung ATY, Wang YY, Wong FS. Assessment of liquefaction potential using neural networks. Soil Dynamics and Earthquake Engineering 1993; 12: 325-35. https://doi.org/10.1016/0267-7261(93)90035-P
[4] Goh ATC. Seismic liquefaction potential assessed by neural networks. J Geotech Geoenviron Eng Div 1994; 120(9): 1467-80.
https://doi.org/10.1061/(ASCE)0733-9410(1994)120:9(1467)
[5] Goh ATC. Neural network modeling of CPT seismic liquefaction data. J Geotech Eng 1996; 122(1): 70-3. https://doi.org/10.1061/(ASCE)0733-9410(1996)122:1(70)
[6] Juang CH, Chen CJ, Tien Y-M. Appraising cone penetration test based liquefaction resistance evaluation methods: artificial neural network approach. Can Geotech J 1999; 36: 443-54.
https://doi.org/10.1139/t99-011
[7] Baziar MH, Nilipour N. Evaluation of liquefaction using neural networks and CPT results. Soil Dynamics and Earthquake Engineering 2003; 23: 631-6.
https://doi.org/10.1016/S0267-7261(03)00068-X
[8] Hanna AM, Ural D, Saygili G. Neural network model for liquefaction potential in soil deposits using Turkey and Taiwan earthquake data. Soil Dynamics and Earthquake Engineering 2007; 27: 521-40. https://doi.org/10.1016/j.soildyn.2006.11.001
[9] Kayadelen C. Soil liquefaction modeling by Genetic Expression Programming and Neuro-Fuzzy. Expert Systems with Applications 2011; 38: 4080-7.[10] Chern SG, Lee CY, Wang CC. CPT-Based Liquefaction Assessment by Using Fuzzy-Neural Network. J Marine Science Tech 2008; 16(2): 139-48.
https://doi.org/10.1016/j.eswa.2010.09.071
[10] Yazdi JS, Kalantary F, Yazdi HS. Prediction of liquefaction potential based on CPT up-sampling. Computers & Geotechnics 2012; 44: 10-23. https://doi.org/10.1016/j.cageo.2012.03.025
[11] Jas K, Dodagoudar GR. Liquefaction Potential Assessment of Soils Using Machine Learning Techniques: A State-of-the-Art Review from 1994-2021. Int J Geomech 2023; 23(7).
https://doi.org/10.1061/IJGNAI.GMENG-7788
[12] Cetin KO, Seed RB, Kiureghian AD, Tokimatsu K, Harder LF, Kayen RE, Moss RES. Standard Penetration Test-Based Probabilistic and Deterministic Assessment of Seismic Soil Liquefaction Potential. J Geotech Geoenviron Eng 2004; 130(12): 1314-40.
https://doi.org/10.1061/(ASCE)1090-0241(2004)130:12(131 4)
[13] Zadeh LA. Fuzzy sets. Inf and Cont 1965; 8: 338-53. https://doi.org/10.1016/S0019-9958(65)90241-X
[14] Bezdek JC. Pattern recognition with fuzzy objective function algorithms. New York: Plenum; 1981. https://doi.org/10.1007/978-1-4757-0450-1
[15] Bezdek JC, Ehrlich R, Full W. FCM: The Fuzzy c-Means Clustering Algorithm. Computers and Geosciences 1984; 10(2-3): 191-203.
https://doi.org/10.1016/0098-3004(84)90020-7
[16] Mohammadikish, S., Ashayeri, I., Biglari, M. et al. Soil Liquefaction Assessment by CPT and VS Data and Incomplete-Fuzzy C-Means Clustering. Geotech Geol Eng 42, 2205-2220 (2024).
https://doi.org/10.1007/s10706-023-02669-1
[17] Boulanger RW, Mejia LH, Idriss IM. Liquefaction at Moss Landing During Loma Prieta Earthquake. J Geotech Geoenviron Eng 1997; 123(5): 453-67. https://doi.org/10.1061/(ASCE)1090-0241(1997)123:5(453)
