...
首页> 外文期刊>Engineering Geology >Investigation for probabilistic prediction of shear strength properties of clay-rich fault gouge in the Austrian Alps
【24h】

Investigation for probabilistic prediction of shear strength properties of clay-rich fault gouge in the Austrian Alps

机译:奥地利阿尔卑斯山富粘土断层泥抗剪强度特性的概率预测研究

获取原文
获取原文并翻译 | 示例
           

摘要

Geotechnical practice in the Austrian Alps determined that it is difficult to evaluate the shear strength properties of clay-rich fault gouge associated with thin shear zones because of a lack of undisturbed samples and the high normal stresses required for appropriate shear tests. The objective of this paper is to do a probabilistic prediction of the shear strength properties of clay-rich fault gouges through simple geotechnical parameters. The representative clay-rich fault gouge samples were selected from engineering projects. Their characteristics, i.e. water content and density, were simulated through high normal stress consolidation. Then they were sheared under different normal stress with low velocities. Shear test results show that there are four types of shear strength properties, which can be classified by geotechnical parameters. The significance of the parameters for the classification was tested. Using the Stepwise Discriminant Analysis, the best predictors were evaluated from basic soil classification parameters. The probabilistic prediction of shear strength properties was established by the combinations of the best predictors using Bayes's theorem. This makes the prediction of the shear strength properties of clay-rich fault gouge in the Austrian Alps easy and practical when sample availability is limited.
机译:奥地利阿尔卑斯山的岩土工程实践确定,由于缺乏原状样品且进行适当的剪切测试需要较高的正应力,因此难以评估与薄剪切带相关的富含粘土的断层泥的剪切强度特性。本文的目的是通过简单的岩土工程参数对富含粘土的断层泥的抗剪强度特性进行概率预测。从工程项目中选择了代表性的富粘土断层泥样品。通过高法向应力固结模拟了它们的特性,即水含量和密度。然后在不同的法向应力下以低速剪切它们。剪切试验结果表明,有四种类型的剪切强度特性,可以根据岩土参数进行分类。测试了参数对于分类的重要性。使用逐步判别分析,从基本土壤分类参数中评估了最佳预测因子。剪切强度特性的概率预测是通过使用贝叶斯定理的最佳预测因子的组合来建立的。当样品数量有限时,这使得在奥地利阿尔卑斯山富含粘土的断层泥的抗剪强度特性的预测变得容易而实用。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号