Energy-based numerical models for assessment of soil liquefaction

By Amir Hossein Alavi, Amir H. Gandomi. 2012

107 citations recorded by the source corpus.

Abstract This study presents promising variants of genetic programming (GP), namely linear genetic programming (LGP) and multi expression programming (MEP) to evaluate the liquefaction resistance of sandy soils. Generalized LGP and MEP-based relationships were developed between the strain energy density required to trigger liquefaction (capacity energy) and the factors affecting the liquefaction characteristics of sands. The correlations were established based on well established and widely dispersed experimental results obtained from the literature. To verify the applicability of the derived models, they were employed to estimate the capacity energy values of parts of the test results that were not included in the analysis. The external validation of the models was verified using statistical criteria recommended by researchers. Sensitivity and parametric analyses were performed for further verification of the correlations. The results indicate that the proposed correlations are effectively capable of capturing the liquefaction resistance of a number of sandy soils. The developed correlations provide a significantly better prediction performance than the models found in the literature. Furthermore, the best LGP and MEP models perform superior than the optimal traditional GP model. The verification phases confirm the efficiency of the derived correlations for their general application to the assessment of the strain energy at the onset of liquefaction.

Subfields: Civil and Structural Engineering

View Paper: https://www.semanticscholar.org/paper/cb9f6624d260961f7a6f44d9779c89f794a9c0d3

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In Civil and Structural Engineering

Citations in the corpus

Machine learning in geosciences and remote sensing (2016, background): “…engineering problems such as liquefaction phenomenon, ground motion parameters, or ground movement patterns (e.g., Javadi et al., 2006; Shuhua et al., 2006; Lia et al., 2007; Cabalar and Cevik, 2009; Alavi et al., 2011b; Gandomi et al., 2011; Alavi and Gandomi, 2012; Gandomi and Alavi, 2013).”