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Hybridizing Genetic Programming with Orthogonal Least Squares for Modeling of Soil Liquefaction

Amir H. Gandomi(1*), Amir H. Alavi(2)

(1) Department of Civil Engineering, University of Akron, United States
(2) Department of Civil and Environmental Engineering, Engineering Building, Michigan State University, United States
(*) Corresponding author

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Precise estimation of the strain energy density required to trigger soil liquefaction, denoted as capacity energy, has been the focus of many studies. The main objective of this paper is to develop a robust prediction model for the soil capacity energy using a novel hybrid technique coupling genetic programming with orthogonal least squares, called GP/OLS. The proposed model was developed upon experimental results collected through a literature review. A traditional genetic programming analysis was performed to benchmark the GP/OLS model. The predictions made by the derived model were found to be more accurate than those provided by the genetic programming and other existing models. A subsequent parametric study was carried out and the trends of the results were confirmed via some previous laboratory studies
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Genetic Programming; Orthogonal Least Square; Soil Liquefaction; Capacity Energy; Formulation

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