Liquefaction, which typically occurs in saturated sandy soil deposits, is one of the destructive phenomena that can occur during an earthquake. When the soil reaches liquefied state, it loses a significant amount of resistance and stiffness, which often results in widespread catastrophic damages. Therefore, accurate evaluating the potential of soil liquefaction occurrence is of great importance in earthquake geotechnical designs in regions prone to this phenomenon. The strain energy-based approach is a novel robustness technique to evaluate liquefaction potential. In the current research, 165 laboratory data sets from cyclic experiments were collected and analyzed. A predictive model using gene expression programming (GEP) was proposed to assess strain energy needed for occurrence of soil liquefaction. Assessing physical behavior of developed GEP-based model was conducted through sensitivity analysis. Performance of GEP-based was validated by comparing with a series of centrifuge experiments and cyclic triaxial tests results. Subsequently, after experimental verification of numerical modeling, the strain energy required for soil liquefaction under cyclic loading at different conditions were numerically evaluated and compared with the strain energy calculated by proposed model. Finally, the developed GEP-based model was compared with established strain energy-based relationships. The results indicated high precision of proposed GEP-based model in determination of strain energy required for soil liquefaction triggering.
