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 (R²) = 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.

在过去的地震中，液化造成了许多灾难。发生地震时，饱和的颗粒状土壤可能会发生液化现象，从而可能造成重大危害。因此，有效可靠地预测土壤液化潜力非常重要，尤其是在设计土木工程项目时。本研究结合最优化算法，即灰狼优化（GWO），差分进化（DE）和遗传算法（GA），开发了最小二乘支持向量机（LSSVM）和径向基函数神经网络（RBFNN）。预测土壤液化潜力。然后，将统计得分（例如均方根误差）用于评估开发的模型。R ²）= 1且均方根误差（RMSE）= 0，比先前在文献中提出的用于预测土壤液化潜力的其他模型产生了更好的结果。它是土壤液化潜力预测的有效替代方法。此外，这项研究的结果证实了GWO算法在训练RBFNN和LSSVM模型方面的有效性。根据敏感性分析结果，循环应力比也被认为是研究土壤液化最有效的参数。