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矿渣与工业废渣改良黄土的性能与机理研究进展
引用本文:朱苗淼,朱武卫. 矿渣与工业废渣改良黄土的性能与机理研究进展[J]. 灾害学, 2022, 0(1): 129-133
作者姓名:朱苗淼  朱武卫
作者单位:陕西省建筑科学研究院有限公司;四主体一联合建筑工程技术研究中心
基金项目:西安市莲湖区重点领域产业链成长企业培育引领项目(莲科发7号)。
摘    要:黄土在世界上分布面积较广,在我国也有约60×104 km2的分布,具有湿陷性是其区别于其它区域性土的最显著特点,并由此引发造成各种地质灾害和工程问题.采用石灰、水泥或高分子材料等对其进行改性以达到消除湿陷性或提高黄土强度的目的,是岩土工程领域的重要课题.选用矿渣和工业废渣作为改性参料,用以研究改性黄土的力学特性变化和改...

关 键 词:矿渣  工业废渣  改良黄土  性能  机理  风险预测

Research Progress on Properties and Mechanism of Loess Modified by Slag and Industrial Waste Residue
ZHU Miaomiao,ZHU Wuwei. Research Progress on Properties and Mechanism of Loess Modified by Slag and Industrial Waste Residue[J]. Journal of Catastrophology, 2022, 0(1): 129-133
Authors:ZHU Miaomiao  ZHU Wuwei
Affiliation:(Shaanxi Academy of Building Research Co.,Ltd.,Xi'an 710082,China;Four-main-one Joint Construction Engineering Technology Research Center,Xi'an 710082,China)
Abstract:Loess is widely distributed in the world and has collapsibility,which is easy to cause geological disasters and engineering problems.In order to avoid economic losses and casualties caused by loess collapsibility,it is often modified by lime,cement and polymer to solidify loess and strengthen the strength of loess.However,the materials used in these methods are limited in the context of energy saving,emission reduction and sustainable development.Using slag and industrial waste as collapsible loess modifiers can not only solve the pollution risk caused by landfill,but also turn waste into treasure and maximize resource utilization.At present,the research in this field at home and abroad is in the initial stage,which mainly focuses on the mechanical properties,mechanism and potential environmental risk prediction in the application of slag and industrial waste slag to improve loess.This paper will summarize and discuss the above three aspects,and point out the possible research and development trends in this field,so as to lay a foundation for further research and application in the future.
Keywords:slag  industrial waste  improved loess  performance  mechanism  risk prediction
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