Lateral displacement due to liquefaction (D_H) is the most destructive effect of earthquakes in saturated loose or semi-loose sandy soil. Among all earthquake parameters, the standardized cumulative absolute velocity (CAV₅) exhibits the largest correlation with increasing pore water pressure and liquefaction. Furthermore, the complex effect of fine content (FC) at different values has been studied and demonstrated. Nevertheless, these two contexts have not been entered into empirical and semi-empirical models to predict D_H This study bridges this gap by adding CAV₅ to the data set and developing two artificial neural network (ANN) models. The first model is based on the entire range of the parameters, whereas the second model is based on the samples with FC values that are less than the 28% critical value. The results demonstrate the higher accuracy of the second model that is developed even with less data. Additionally, according to the uncertainties in the geotechnical and earthquake parameters, sensitivity analysis was performed via Monte Carlo simulation (MCS) using the second developed ANN model that exhibited higher accuracy. The results demonstrated the significant influence of the uncertainties of earthquake parameters on predicting D_H.

由于液化（横向位移d _ħ）是松散饱和或半松散砂土地震的最具破坏性的影响。在所有地震参数中，标准化的累积绝对速度（CAV ₅）与增加的孔隙水压力和液化表现出最大的相关性。此外，邻复杂效果˚F细粒含量（FC为不同的值）已被研究并证实。然而，这两个背景尚未进入经验和半经验模型来预测D _H。本研究通过添加CAV ₅弥合了这一差距。数据集并开发两个人工神经网络（ANN）模型。第一个模型基于参数的整个范围，而第二个模型基于FC值小于28％临界值的样本。结果表明，即使数据量较少，开发的第二个模型的准确性也更高。此外，根据岩土和地震参数的不确定性，使用第二个开发的具有较高准确性的ANN模型，通过蒙特卡罗模拟（MCS）进行了敏感性分析。结果表明，地震参数的不确定性对D _H的预测有重大影响。