首页 | 本学科首页   官方微博 | 高级检索  
文章检索
  按 检索   检索词:      
出版年份:   被引次数:   他引次数: 提示:输入*表示无穷大
  收费全文   3篇
  免费   0篇
地质学   3篇
  2014年   1篇
  2008年   1篇
  2001年   1篇
排序方式: 共有3条查询结果,搜索用时 31 毫秒
1
1.
软土路堤填料二次加灰处理技术探讨   总被引:1,自引:0,他引:1  
目前在江苏高速公路建设中对软土改性处理多采用二次加灰技术,这一处理技术不仅有效解决了软土水分高、土块粉碎困难、掺灰不均、施工速度慢、易造成石灰失效等技术问题,而且可以提高灰土的强度,工程实践表明,其CBR强度可以提高6%~8%。本文就软土改性二次加灰技术的机理作深入探讨,研究表明,第一次加灰,石灰消解既可以吸收土中水分,所产生的热量又可以加快水分蒸发,同时Ca离子随水进入黏土矿物颗粒表面进行离子交换;第二次加灰,通过压实使石灰与软土进一步发生离子交换和火山灰反应,形成新的结晶体和结构,提高灰土的强度和稳定性。  相似文献   
2.
粉煤灰处理软土地基的试验研究   总被引:4,自引:1,他引:4  
利用淤泥质粘土掺入不同比例的粉煤灰, 分别进行了渗透、固结、直接剪切及三轴剪切试验, 详细研究了粘土掺入粉煤灰后其工程力学性能变化的特征以及粉煤灰加固软土的机理。试验结果证实, 利用粉煤灰处理软土地基是-种所需投资较少且有明显效果的方法, 并可带来良好的环境效益和社会效益。  相似文献   
3.
Learning from data is a very attractive alternative to “manually” learning. Therefore, in the last decade the use of machine learning has spread rapidly throughout computer science and beyond. This approach, supported on advanced statistics analysis, is usually known as Data Mining (DM) and has been applied successfully in different knowledge domains. In the present study, we show that DM can make a great contribution in solving complex problems in civil engineering, namely in the field of geotechnical engineering. Particularly, the high learning capabilities of Support Vector Machines (SVMs) algorithm, characterized by it flexibility and non-linear capabilities, were applied in the prediction of the Uniaxial Compressive Strength (UCS) of Jet Grouting (JG) samples directly extracted from JG columns, usually known as soilcrete. JG technology is a soft-soil improvement method worldwide applied, extremely versatile and economically attractive when compared with other methods. However, even after many years of experience still lacks of accurate methods for JG columns design. Accordingly, in the present paper a novel approach (based on SVM algorithm) for UCS prediction of soilcrete mixtures is proposed supported on 472 results collected from different geotechnical works. Furthermore, a global sensitivity analysis is applied in order to explain and extract understandable knowledge from the proposed model. Such analysis allows one to identify the key variables in UCS prediction and to measure its effect. Finally, a tentative step toward a development of UCS prediction based on laboratory studies is presented and discussed.  相似文献   
1
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号