Support vector machines: Their use in geotechnical engineering as illustrated using seismic liquefaction data

By A. Goh, S. Goh. 2007

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Energy-based numerical models for assessment of soil liquefaction (2012, methodology): “Also, they have been used for the evaluation of the liquefaction potential (Goh, 1994; Goh, 2002; Cetin et al., 2004; Pal, 2006; Goh and Goh, 2007; Samui, 2007; Oommen et al., 2008; Oommen and Baise, 2010; Oommen et al., 2010).”
The promise of implementing machine learning in earthquake engineering: A state-of-the-art review (2020, methodology): “Significant studies in this area include the implementations of SVM (Goh and Goh, 2007; Pal, 2006), ANN (Goh, 1994, 1996; Hanna et al., 2007; Juang and Chen, 1999; Ramakrishnan et al., 2008; U¨lgen and Engin, 2007), a combination of kernel Fisher discriminant analysis (KFDA) with SVM (Hoang and…”
Efficient reliability method for implicit limit state surface with correlated non‐Gaussian variables (2015, methodology): “RSM aims at approximating the actual LSS using closed-form expressions by simple polynomials [3–6], Hermite polynomial chaos expansion [7, 8], neural networks [9–11], and support vector machines [12–14].”