Integrating the LSSVM and RBFNN models with three optimization algorithms to predict the soil liquefaction potential

Liquefaction has caused many catastrophes during earthquakes in the past. When an earthquake is occurring, saturated granular soils may be subjected to the liquefaction phenomenon that can result in significant hazards. Therefore, a valid and reliable prediction of soil liquefaction potential is of high importance, especially when designing civil engineering projects. This study developed the least squares support vector machine (LSSVM) and radial basis function neural network (RBFNN) in combination with the optimization algorithms, i.e., the grey wolves optimization (GWO), differential evolution (DE), and genetic algorithm (GA) to predict the soil liquefaction potential. Afterwards, statistical scores such as root mean square error were applied to evaluate the developed models. The computational results showed that the proposed RBFNN-GWO and LSSVM-GWO, with Coefficient of Determination (R-2) = 1 and Root Mean Square Error (RMSE) = 0, produced better results than other models proposed previously in the literature for the prediction of the soil liquefaction potential. It is an efficient and effective alternative for the soil liquefaction potential prediction. Furthermore, the results of this study confirmed the effectiveness of the GWO algorithm in training the RBFNN and LSSVM models. According to sensitivity analysis results, the cyclic stress ratio was also found as the most effective parameter on the soil liquefaction in the studied case.

Automated summary

This study developed and evaluated models to predict soil liquefaction potential using least squares support vector machine and radial basis function neural network with optimization algorithms. The proposed models outperformed previous ones, with cyclic stress ratio identified as the most important parameter impacting soil liquefaction.