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本文利用静力触探（ＣＰＴ）场地液化数据，建立了液化势判定的反向传播神经网络模型。研究表明，同传统方法相比，人工神经网络方法在判别砂土液化势方面是可行的。
In this paper, the back-propagating neural network model of liquefaction potential is established by using the static leaching (CPT) site liquefaction data. The research shows that, compared with the traditional method, artificial neural network method is feasible in judging the liquefaction potential of sand.