Smart prediction of liquefaction-induced lateral spreading

The prediction of liquefaction-induced lateral spreading/displacement (D_h) is a challenging task for civil/geotechnical engineers. In this study, a new approach is proposed to predict D_h using gene expression programming (GEP). Based on statistical reasoning, individual models were developed for two topographies: free-face and gently sloping ground. Along with a comparison with conventional approaches for predicting the D_h, four additional regression-based soft computing models, i.e. Gaussian process regression (GPR), relevance vector machine (RVM), sequential minimal optimization regression (SMOR), and M5-tree, were developed and compared with the GEP model. The results indicate that the GEP models predict D_h with less bias, as evidenced by the root mean square error (RMSE) and mean absolute error (MAE) for training (i.e. 1.092 and 0.815; and 0.643 and 0.526) and for testing (i.e. 0.89 and 0.705; and 0.773 and 0.573) in free-face and gently sloping ground topographies, respectively. The overall performance for the free-face topology was ranked as follows: GEP > RVM > M5-tree > GPR > SMOR, with a total score of 40, 32, 24, 15, and 10, respectively. For the gently sloping condition, the performance was ranked as follows: GEP > RVM > GPR > M5-tree > SMOR with a total score of 40, 32, 21, 19, and 8, respectively. Finally, the results of the sensitivity analysis showed that for both free-face and gently sloping ground, the liquefiable layer thickness (T₁₅) was the major parameter with percentage deterioration (%D) value of 99.15 and 90.72, respectively.