节点文献

基于Matlab对强夯法处理黄土湿陷性的微观结构研究

Microstructural research using Matlab on collapsible loess by dynamic compaction method

  • 推荐 CAJ下载
  • PDF下载
  • 不支持迅雷等下载工具,请取消加速工具后下载。

【作者】 苗得雨马富丽王敏白晓红

【Author】 Miao Deyu,Ma Fuli,Wang Min,Bai Xiaohong(College of Architecture and Civil Engineering,Taiyuan University of Technology,Taiyuan 030024,China)

【机构】 太原理工大学建筑与土木工程学院

【摘要】 黄土的湿陷性与其微观结构有极大关系,土体的物理力学性状也很大程度上受其微观结构影响。针对湿陷性黄土微观研究中的问题,对山西省平阳高速中强夯法处理湿陷性黄土进行微观层面上的定性和定量分析。从土样提取与制备到SEM图像扫描,开展了详实的探讨。针对SEM图像的特征及其处理过程中存在的问题,使用Matlab对图像预处理中去除亮度差异背景,对比度增强,降噪做了对比分析,通过预处理可以对图像信息在采集阶段较为精确地进行统计计算分析,对比分析了强夯前后的孔隙数量及面积分布、定向特征、形状特征等参数,对土体SEM图像处理和强夯法消除湿陷性机理做较为深入的探讨,为工程建设提供参考依据。

【Abstract】 Collapsibility of loess is related to its microstructure.The physical and mechanical properties of soil are influenced by its microstructure to a large extent.For the problem of collapsibility of loess in microscopic study,we did the qualitative and quantitative analyses of collapsible loess at a microscopic level.The samples that underwent dynamic compaction were taken from Pingyang Highway in Shanxi.Extraction and preparation of soil samples were introduced and scanning electron microscope(SEM) images were obtained.For the features and problems found in the SEM images processing,we applied Matlab for comparative analysis of removal of background brightness difference,contrast enhancement and noise reduction in image processing.The image preprocessing helped for more accurate statistical calculation during the image information acquisition process.Comparing characteristics of pore size and size distribution,directional and shape parameters before and after the dynamic compaction,we further discuss the SEM image processing and the mechanism that uses dynamic consolidation method to remove collapsibility,attempting to provide a reference for engineering construction.

【基金】 国家自然科学基金资助项目(51178287);山西省自然科学基金项目(2010011029);太原理工大学研究生创新基金项目(2012B015)
  • 【文献出处】 中国科技论文 ,China Sciencepaper , 编辑部邮箱 ,2013年05期
  • 【分类号】TU444
  • 【被引频次】15
  • 【下载频次】348
节点文献中: 

本文链接的文献网络图示:

本文的引文网络