长江科学院院报 ›› 2020, Vol. 37 ›› Issue (6): 153-155,178.DOI: 10.11988/ckyyb.20190094

• 岩土工程 • 上一篇    下一篇

基于Logistic回归模型的膨胀土判别与分类

高卫东   

  1. 江苏师范大学 地理测绘与城乡规划学院,江苏 徐州 221116
  • 收稿日期:2019-01-23 出版日期:2020-06-01 发布日期:2020-06-21
  • 作者简介:高卫东(1967-),男,安徽怀宁人,副教授,主要从事地质工程等领域研究。E-mail: gwd6771@126.com

Discrimination and Classification of Expansive Soil Based on Logistic Regression Model

GAO Wei-dong   

  1. School of Geography, Geomatics and Planning, Jiangsu Normal University, Xuzhou 221116, China
  • Received:2019-01-23 Online:2020-06-01 Published:2020-06-21

摘要: 采用《公路工程地质勘察规范》(JTG C20—2011)推荐的自由膨胀率、塑性指数、标准吸湿含水率作为膨胀土判别与分类指标,将膨胀潜势分为非、弱、中等、强膨胀土4个等级。以某高速公路沿线土样为例,利用SPSS软件建立了土膨胀潜势分级的有序Logistic回归模型,并利用所建模型对待判土样进行判别,结果与实际一致。研究结果表明:有序Logistic回归模型的判别性能良,能客观反映膨胀土分类的复杂状况,具有较好的工程应用前景。

关键词: 膨胀土, 膨胀潜势, 有序Logistic回归, 判别与分类, SPSS

Abstract: According to Code for highway engineering geological investigation (JTG C20—2011), three indices, namely, free expansion rate, plasticity index, and water content of soil under standard moisture absorption, were selected as the factors for synthetic evaluation of expansive soil. The swelling potential of expansive soil was divided into four grades: non-expansive, weakly expansive soil, moderately expansive, and strongly expansive. With the soil samples along a highway as an example, the ordinal Logistic regression model of swelling potential classification was established by using SPSS software and was applied to testing other cases. The predicted results were in good agreement with the actual. The results indicate that the ordinal Logistic regression model performs excellently, and can objectively reflect the complicated situation of expansive soil classification, thus is of good prospect in practical engineering.

Key words: expansive soil, swelling potential, ordinal Logistic regression, discrimination and classification, SPSS

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