基于人工神经网络的膨胀土判别分类方法：以宁连一级公路为例

以往用于膨胀土判别分类的定量手段，如模糊数学，灰色聚类法等都或多或少带有一定的人为因素，不可避免的影响了分类结果，本文提出了一种膨胀土判别分类的新方法－ＢＰ神经网络模型，较好的弥补了这方面的缺点，并具有容错能力强，客观性好等特点，研究表明，这种方法在实践中合理的，可行的。

METHOD FOR EXPANSIVE SOIL CLASSIFICATION BASED ON THE ARTIFICIAL NEURAL NETWORK EXEMPLIFIED BY NING LIAN HIGHWAY

Previous quantitative methods for classifying expansive soil such as “Fuzzy” and “Grey Cluster Analysis” have certain shortcomings．Both of them rely on artificial factors and thus may influent the true results of the soil classification．A new method of Artificia1 Neura1 Network (ANN ) model presented in the paper can well solve the difficulties and it has good objectivity and excellent disturbance resistance．The model is applied for classifying soil samples of D section in Ning-Lian high-grade highway．The results are well in accord with those by “Fuzzy” and Grey Cluster Analysis” methods．The ANN method has been approved available and reasonable in practice and may become an effective way on determining expansive soil’s properties quantitatively．