Soil liquefaction is one of recognized nonlinear devastating types of ground failures associated with earthquakes. The analyses frameworks for this phenomenon have been addressed using different methods and correlated triggering factors in case histories. In the current paper, a hybrid model using imperialistic competitive metaheuristic algorithm (ICA) incorporated with multi-objective generalized feedforward neural network (MOGFFN) for the purpose of liquefaction potential analysis was assessed. The optimum hybrid ICA-MOGFFN model was applied on a diversified database of 296 compiled case histories comprising nine of the most significant effective parameters on liquefaction. The result of ICA-MOGFFN model demonstrated for 3.01%, 2.09% and 7.46% progress in the success rates for the safety factor, liquefaction occurrence and depth of liquefaction. Accordingly, the conducted precision–recall curves showed 5.08%, 1.73% and 3.92% improvement compared to MOGFFN. Further evaluations using different statistical metrics represented superior progress in performance of hybrid ICA-MOGFFN. The capability of the developed method then was approved from observed agreement with other accepted procedures. The results implied that the developed hybrid model was a flexible and accurate enough tool that can effectively be applied for the liquefaction potential analyses. Using sensitivity analyses, the most and least effective inputs on the predicted liquefaction parameters were identified.

土壤液化是公认的与地震有关的非线性破坏性破坏类型之一。在案例历史中，已使用不同的方法和相关的触发因素来解决此现象的分析框架。为了评估液化潜力，本文评估了一种使用帝国竞争元启发式算法（ICA）并结合多目标广义前馈神经网络（MOGFFN）的混合模型。最佳混合ICA-MOGFFN模型应用于296个病历的多元化数据库，其中包括9个最重要的液化有效参数。ICA-MOGFFN的结果该模型证明了安全系数，液化发生和液化深度的成功率分别为3.01，％，2.09％和7.46％。因此，与MOGFFN相比，进行的精确度-召回曲线显示提高了5.08％，1.73％和3.92％。使用不同的统计指标进行的进一步评估代表了混合ICA-MOGFFN性能的卓越进步。然后，从观察到的同意与其他可接受的程序中批准了所开发方法的功能。结果表明，所开发的混合模型是一种灵活且足够准确的工具，可以有效地用于液化潜力分析。使用敏感性分析，确定了预测液化参数上最有效的输入。