方法：应用人工神经网络误差反向传播模型进行含量测定。

Method:Artificial neural network based on error back propagation was used.

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本文根据人工神经网络的一典型模型—反向传播模型，以及地震荷载下的各项土的物理—力学参数，建立了土液化类型的神经网络数学模型。

A typical artificial neural network model-back-propagation model was presented for prediction on the soil liquefaction type based on the physical parameters of soils under earthquake.

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利用误差反向传播的改进算法对样本数据进行训练，并用另外的一些样本数据验证模型的应用效果。

Using the improved error backward propagation, the model is trained with stylebook data and validated its effect by other stylebook data.

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