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作者投稿 专家审稿 编辑办公 编委办公 主编办公

冰川冻土 ›› 2006, Vol. 28 ›› Issue (4): 607-612.

• 寒区工程与技术 • 上一篇    下一篇


宋启卓, 陈龙珠   

  1. 上海交通大学, 土木系安全与防灾工程研究所, 上海, 200030
  • 收稿日期:2005-11-11 修回日期:2006-06-22 出版日期:2006-08-25 发布日期:2012-04-26
  • 通讯作者: 陈龙珠, E-mail:lzchen@sjtu.edu.cn E-mail:lzchen@sjtu.edu.cn

Application of Artificial Neural Network to Studying Salt Expansion Properties of Saline Soil

SONG Qi-zhuo, CHEN Long-zhu   

  1. Institute of Engineering Safety and Disaster Prevention, Shanghai Jiaotong University, Shanghai 200030, China
  • Received:2005-11-11 Revised:2006-06-22 Online:2006-08-25 Published:2012-04-26

摘要: 利用人工神经网络处理非线性体系的优势性,对盐渍土膨胀规律多影响因素试验数据进行了建模方法分析,提出了盐渍土盐胀率随含水量、氯化钠含量、硫酸钠含量、初始干容重和上覆荷载5因素变化的计算公式,计算结论比常规二次回归法更加符合目前对盐渍土盐胀规律的定性认识.

关键词: 人工神经网络, 盐渍土, 盐胀率, 交互作用

Abstract: A non-linear neural network model is established to study the salt expansion properties of saline soil under the function of the five factors,i.e., water content,NaCl concentration,Na2SO4 concentration,initial dry density and overburden pressure of saline soil,based on the documents mentioned in this paper.As compared with the traditional method of quadratic stepwise regression,it shows much more advantages and creditability in solving the problem of the non-linear interaction of multi influencing factors.At the same time,the formula of counting the expansion rate of saline soil under the function of the five factors is updated and coincided the present understanding of the properties of saline soil.

Key words: artificial neural network, saline soil, salt expansion rate, interaction


  • P642.13