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突变级数法在边坡稳定综合评判中的应用
引用本文:梁桂兰,徐卫亚,何育智,赵延喜.突变级数法在边坡稳定综合评判中的应用[J].岩土力学,2008,29(7):1895-1899.
作者姓名:梁桂兰  徐卫亚  何育智  赵延喜
作者单位:1. 河海大学岩土工程研究所,南京,210098
2. 南京市民用建筑设计研究院有限责任公司,南京,210002
基金项目:国家自然科学基金 , 国家科技支撑计划
摘    要:边坡的稳定性受地质、工程等众多因素影响,且其变形破坏机理复杂,危害巨大,因而边坡稳定问题分析研究一直是理论和实践研究的热点.引入突变理论对边坡的稳定性问题进行研究,利用突变数学和模糊数学结合引出的突变级数法,提出边坡稳定性评判指标--突变级数, 从而建立了新的稳定性评判模型.基于突变级数法的边坡稳定性评判法,综合考虑了边坡各种要素对其稳定性的不同影响程度,真实地描绘了边坡系统的稳定状态.实例分析结果表明,该方法评判结果准确,精度较高,具有良好的实际应用价值.

关 键 词:边坡稳定  综合评判  突变理论  模糊数学  突变级数法  突变级数法  边坡稳定性  合评判  应用  slope  stability  comprehensive  evaluation  method  progression  catastrophe  价值  精度  评判结果  方法  实例分析  稳定状态  边坡系统  程度  影响  要素  综合
收稿时间:2006-06-22

Application of catastrophe progression method to comprehensive evaluation of slope stability
LIANG Gui-lan,XU Wei-ya,HE Yu-zhi,ZHAO Yan-xi.Application of catastrophe progression method to comprehensive evaluation of slope stability[J].Rock and Soil Mechanics,2008,29(7):1895-1899.
Authors:LIANG Gui-lan  XU Wei-ya  HE Yu-zhi  ZHAO Yan-xi
Institution:1. Research Institute of Geotechnical Engineering , Hohai University, Nanjing 210098, China; 2. Nanjing Municipal Institute of Civil Architecture Co., Ltd, Nanjing 210002, China
Abstract:A slope has complex failure mechanism, and its stability is affected by many factors such as geological and engineering factors. Therefore, more attentions are paid to study of slope stability. A catastrophe model basing on the catastrophe theory and fuzzy mathematics is used to analyze and evaluate the slope stability; and a new comprehensive evaluation index of slope stability is proposed: catastrophe progression. This index considers synthetically effect of many factors on slope stability and reflects actually state of slope system. The study results of engineering cases show that this comprehensive evaluation method of slope stability based on catastrophe progression method has higher accuracy and good application to slope stability analysis.
Keywords:slope stability  comprehensive evaluation  catastrophe theory  fuzzy mathematics  catastrophe progression method  
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