砂土地震液化预测的人工神经网络模型

在简要分析BP算法的基础上，应用BP网络的理论与方法，选取烈度、震中距、平均粒径、不均匀系数、地下水埋深、砂层埋深、标贯击数、剪应力比等8个实测指标，建立了砂土液化预测的神经网络模型。通过实例计算与模型评价、验证了该模型的科学性、高效性并较规范法、Seed简化法等传统方法具有更高的预测精度，说明人工神经网络是解决非线性问题的有效方法之一。

Artificial neural network model for prediction of seismic liquefaction of sand soil

Based on simply analyzing the back propagation algorithm, the principles of the BP neural network are applied to predicting sand liquefaction with eight factors listed as follows: seismic intensity, epicenter distance, mean diameter, coefficient of non-uniformity, underground water depth, sand depth, blow number of standard penetration test, ratio of shearing stress. Through computing practical examples and assessing the model, the model is manifested to be scientific and effective with much more accurate results than the Norm method and seed simplified method. The results show that the method is an efficient one in solving nonlinear problems.