| 基于克里金模型的边坡稳定可靠度分析方法 |
| |
| 引用本文： | 罗正东,董 辉,陈 铖,苏永华.基于克里金模型的边坡稳定可靠度分析方法[J].岩土力学,2015,36(Z1):439-444. |
| |
| 作者姓名： | 罗正东 董 辉 陈 铖 苏永华 |
| |
| 作者单位： | 1. 湘潭大学 土木工程与力学学院，湖南 湘潭 411105；2. 湖南大学 岩土工程研究所，湖南 长沙 410082 |
| |
| 基金项目： | 国家自然科学基金（No.51108397）；湖南省自然科学基金（No.2015JJ2136）；湘潭大学人才引进基金（No.KZ08026） |
| |
| 摘 要： | 边坡工程的复杂性不仅表现为各岩土参数的变异性和非确定性，而且还在于其极限状态功能函数的非解析性及隐式性。以Janbu 法为例，研究了隐式功能函数下易于执行的边坡工程稳定可靠度计算方法。首先，调用边坡极限平衡模式获得岩土基本参数，并利用拉丁超立方试验设计抽取影响边坡稳定性基本参数的适量初始样本。其次，采用地质统计学中的克里金（Kriging）各向异性关联映射方法，将边坡功能函数值表述为随机过程。然后，结合主动学习方法，基于搜索规则调整训练样本，通过反复迭代循环确定满足实际工程精度的随机过程所表示的边坡功能函数。最后，调用随机过程函数通过验算点法（JC法）获得边坡的失效概率。工程算例分析表明，文中方法的求解精度与蒙特卡洛模拟方法相当，但计算过程简明，效率高，更具工程实用性。
|
| 关 键 词： | 边坡工程 克里金（Krigin）模型 拉丁超立方设计 最可能失效区域 主动学习 |
| 收稿时间： | 2015-03-09 |
An analytic method for slope stability reliability based on Kriging model |
| |
| LUO Zheng-dong,DONG Hui,CHEN Cheng,SU Yong-hua.An analytic method for slope stability reliability based on Kriging model[J].Rock and Soil Mechanics,2015,36(Z1):439-444. |
| |
| Authors: | LUO Zheng-dong DONG Hui CHEN Cheng SU Yong-hua |
| |
| Institution: | 1. School of Civil Engineering and Mechanics, Xiangtan University, Xiangtan, Hunan 411105, China;
2. Institute of Geotechnical Engineering, Hunan University, Changsha, Hunan 410082, China |
| |
| Abstract: | The complexity of slope engineering is not only reflected in the variability of geotechnical parameters, but also in the implicit and nonanalytic properties of the performance function. Therefore, the direct calculation of slope stability reliability under implicit performance function that based on Janbu method is researched. First, the slope limit equilibrium model is called to obtain basic geotechnical parameters, and then the initial samples that affect slope stability is acquired through the Latin hypercube sampling. Secondly, using the Kriging anisotropic association mapping method dependence mapping method, the slope function value is expressed as a function of the random process. Then combined with the active learning method, and based on the searching rules to adjust the training samples, the slope performance functions denoted by the random process that meet the accuracy of the actual project is determined through iterative cycle. Finally, the random process function is called to work out the failure probability of slopes through the checking point method (JC method). The case study shows that the accuracy of the proposed method is equivalent to that of the Monte Carlo simulation; but the calculation process is simple, highly efficient, and more practicable. |
| |
| Keywords: | slope engineering Kriging model Latin hypercube design most failure zone active learning |
| 本文献已被 CNKI 等数据库收录！ |
| 点击此处可从《岩土力学》浏览原始摘要信息 |
| 点击此处可从《岩土力学》下载免费的PDF全文 |
|
