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1.
提出了基于支持向量机（SVM）的边坡可靠度分析新算法。该方法采用均匀设计确定样本点,通过一定数量的确定性计算来训练SVM,拟合边坡的功能函数;采用一阶可靠度方法（FORM）和迭代算法优化SVM模型,获得可靠度指标和验算点信息;在SVM模型基础上进一步通过二阶可靠度方法（SORM）和蒙特卡罗模拟（MCS）计算边坡的失稳概率。以两个典型边坡为例,通过与其他方法比较,证明了该方法的准确性和高效性。结果表明：提出的在标准正态空间（U空间）中取样并构建SVM,在原始空间（X空间）中计算功能函数的算法,有效地解决了具有相关非正态分布变量的可靠度分析问题,并且可很容易扩展到SORM的计算。算例结果证明,该方法的精度高于FORM;而效率优于MCS。分析过程中,边坡安全系数计算和可靠度分析相互独立。因此,该方法既适用于具有显式功能函数的简单问题,也适用于需要软件计算安全系数的实际边坡问题。 相似文献
2.
针对传统响应面法在求解具有高度非线性隐式功能函数边坡可靠性问题上的局限性,采用适用于处理高维度、小样本、非线性回归问题的高斯过程回归模型构建隐式功能函数的响应面,将高斯过程响应面与蒙特卡罗模拟法相结合,通过构造合理的迭代方式,在利用高斯过程回归模型的不确定性评价功能获取最优采样点的基础上,实现了高斯过程响应面动态更新,由此提出了边坡失效概率快速估计的高斯过程动态响应面法。利用数值算例验证了该方法的有效性,在此基础上对3个边坡算例进行了可靠性分析。结果表明,与传统响应面法相比较,该方法计算精度与计算效率明显较高,易于与既有的边坡分析软件相结合,且实现容易,适用于边坡可靠性的快速分析。 相似文献
3.
Slope reliability analysis using a support vector machine 总被引：6，自引：0，他引：6
The first-order second-moment method (FOSM) reliability analysis is commonly used for slope stability analysis. It requires the values and partial derivatives of the performance function with respect to the random variables for the design. Such calculations can be cumbersome when the performance functions are implicit. Implicit performance functions are normally encountered when the slope is geologically complicated and the limit equilibrium method (LEM) is used for the stability analysis.
To address this issue, this paper presents a support vector machine (SVM)-based reliability analysis method which combines the SVM with the FOSM. This method employs the SVM method to approximate the implicit performance functions, thus arriving at SVM-based explicit performance functions. The SVM method uses a small set of the actual values of the performance functions obtained via the LEM for complicated slope engineering. Using the SVM model, a large number of values and partial derivatives of the performance functions can be obtained for conventional reliability analysis using the FOSM. Examples are given to illustrate the proposed SVM-based slope reliability analysis. The results show that the proposed approach is applicable to slope reliability analysis which involves implicit performance functions. 相似文献
4.
Enhancement of random finite element method in reliability analysis and risk assessment of soil slopes using Subset Simulation 总被引：4，自引：2，他引：2
Random finite element method (RFEM) provides a rigorous tool to incorporate spatial variability of soil properties into reliability analysis and risk assessment of slope stability. However, it suffers from a common criticism of requiring extensive computational efforts and a lack of efficiency, particularly at small probability levels (e.g., slope failure probability P f ?<?0.001). To address this problem, this study integrates RFEM with an advanced Monte Carlo Simulation (MCS) method called “Subset Simulation (SS)” to develop an efficient RFEM (i.e., SS-based RFEM) for reliability analysis and risk assessment of soil slopes. The proposed SS-based RFEM expresses the overall risk of slope failure as a weighed aggregation of slope failure risk at different probability levels and quantifies the relative contributions of slope failure risk at different probability levels to the overall risk of slope failure. Equations are derived for integrating SS with RFEM to evaluate the probability (P f ) and risk (R) of slope failure. These equations are illustrated using a soil slope example. It is shown that the P f and R are evaluated properly using the proposed approach. Compared with the original RFEM with direct MCS, the SS-based RFEM improves, significantly, the computational efficiency of evaluating P f and R. This enhances the applications of RFEM in the reliability analysis and risk assessment of slope stability. With the aid of improved computational efficiency, a sensitivity study is also performed to explore effects of vertical spatial variability of soil properties on R. It is found that the vertical spatial variability affects the slope failure risk significantly. 相似文献
5.
This paper develops a risk de-aggregation and system reliability approach to evaluate the slope failure probability, pf, using representative slip surfaces together with MCS. An efficient procedure is developed to strategically select the candidate representative slip surfaces, and a risk de-aggregation approach is proposed to quantify contribution of each candidate representative slip surface to the pf, identify the representative slip surfaces, and determine how many representative slip surfaces are needed for estimating the pf with reasonable accuracy. Risk de-aggregation is performed by collecting the failure samples generated in MCS and analyzing them statistically. The proposed methodology is illustrated through a cohesive soil slope example and validated against results from previous studies. When compared with the previous studies, the proposed approach substantially improves the computational efficiency in probabilistic slope stability analysis. The proposed approach is used to explore the effect of spatial variability on the pf. It is found that, when spatial variability is ignored or perfect correlation assumed, the pf of the whole slope system can be solely attributed to a single representative slip surface. In this case, it is theoretically appropriate to use only one slip surface in the reliability analysis. As the spatial variability becomes growingly significant, the number of representative slip surfaces increases, and all representative slip surfaces (i.e., failure modes) contribute more equally to the overall system risk. The variation of failure modes has substantial effect on the pf, and all representative surfaces have to be incorporated properly in the reliability analysis. The risk de-aggregation and system reliability approach developed in this paper provides a practical and efficient means to incorporate such a variation of failure modes in probabilistic slope stability analysis. 相似文献
6.
System effects should be considered in the probabilistic analysis of a layered soil slope due to the potential existence of multiple failure modes. This paper presents a system reliability analysis approach for layered soil slopes based on multivariate adaptive regression splines (MARS) and Monte Carlo simulation (MCS). The proposed approach is achieved in a two-phase process. First, MARS is constructed based on a group of training samples that are generated by Latin hypercube sampling (LHS). MARS is validated by a specific number of testing samples which are randomly generated per the underlying distributions. Second, the established MARS is integrated with MCS to estimate the system failure probability of slopes. Two types of multi-layered soil slopes (cohesive slope and c–φ slope) are examined to assess the capability and validity of the proposed approach. Each type of slope includes two examples with different statistics and system failure probability levels. The proposed approach can provide an accurate estimation of the system failure probability of a soil slope. In addition, the proposed approach is more accurate than the quadratic response surface method (QRSM) and the second-order stochastic response surface method (SRSM) for slopes with highly nonlinear limit state functions (LSFs). The results show that the proposed MARS-based MCS is a favorable and useful tool for the system reliability analysis of soil slopes. 相似文献
7.
8.
This study aims to extend the multivariate adaptive regression splines(MARS)-Monte Carlo simulation(MCS) method for reliability analysis of slopes in spatially variable soils. This approach is used to explore the influences of the multiscale spatial variability of soil properties on the probability of failure(P_f) of the slopes. In the proposed approach, the relationship between the factor of safety and the soil strength parameters characterized with spatial variability is approximated by the MARS, with the aid of Karhunen-Loeve expansion. MCS is subsequently performed on the established MARS model to evaluate Pf.Finally, a nominally homogeneous cohesive-frictional slope and a heterogeneous cohesive slope, which are both characterized with different spatial variabilities, are utilized to illustrate the proposed approach.Results showed that the proposed approach can estimate the P_f of the slopes efficiently in spatially variable soils with sufficient accuracy. Moreover, the approach is relatively robust to the influence of different statistics of soil properties, thereby making it an effective and practical tool for addressing slope reliability problems concerning time-consuming deterministic stability models with low levels of P_f.Furthermore, disregarding the multiscale spatial variability of soil properties can overestimate or underestimate the P_f. Although the difference is small in general, the multiscale spatial variability of the soil properties must still be considered in the reliability analysis of heterogeneous slopes, especially for those highly related to cost effective and accurate designs. 相似文献
9.
提出了一套基于随机响应面法的边坡系统可靠度分析方法。该方法首先从大量潜在滑动面中筛选出代表性滑动面。针对每条代表性滑动面,采用Hermite多项式展开建立其安全系数与土体参数间的非线性显式函数关系（即随机响应面）。然后,采用直接蒙特卡洛模拟计算边坡系统失效概率。在蒙特卡罗模拟中,采用所有代表性滑动面的随机响应面计算每一组样本所对应的边坡最小安全系数。最后,以两个典型多层边坡系统可靠度问题为例验证了该方法的有效性。结果表明：文中提出的边坡系统可靠度分析方法能够有效地识别边坡代表性滑动面,具有较高的计算精度和效率,并且确定代表性滑动面时无需计算滑动面间的相关系数。同时该方法可以有效地计算低失效概率水平的边坡系统可靠度,为含相关非正态参数的边坡系统可靠度问题提供了一条有效的分析途径。此外,多层边坡可能同时存在多条潜在滑动面,基于单一滑动面（如临界确定性滑动面）或者部分代表性滑动面进行边坡系统可靠度分析均会低估边坡失效概率。 相似文献
10.
多层土坡在岩土工程实际中十分常见,不仅土体参数存在一定的空间变异性,而且土体框架呈现明显的层状分布特征,然而目前对考虑土体参数空间变异性的多层土坡稳定可靠度研究的远远不够。提出了基于多重响应面边坡系统可靠度分析的蒙特卡洛模拟（MCS）方法,给出了计算流程图,系统地研究了考虑土体参数空间变异性的多层土坡系统可靠度问题。结果表明,提出方法能够有效地分析考虑参数空间变异性低失效概率水平的多层土坡系统可靠度问题,并且具有较高的参数敏感性分析计算效率。 相似文献
11.
Reliability-based analysis of cantilever retaining walls requires consideration of different failure mechanisms. In this paper, the reliability of soil-wall system is assessed considering two failure modes: rotational and structural stability, and the system reliability is assumed as a series system. The methodology is based on Monte Carlo Simulation (MCS), and it deals with the variability of the design parameters in the limit equilibrium analysis of a wall embedded in granular soil. Results of the MCS indicate that the reliability of the failure components increases exponentially by increasing the variability of design parameters. The results of the system reliability indicate how the system reliability is different from the component reliabilities. The strength of the weakest component influences the reliability of the system. The system reliability index increases with the wall section gradually. However it remains constant for the rotational failure mode. 相似文献
12.
Correlated random variables are commonly involved in probabilistic slope stability analysis, such as reliability analysis of slopes with spatially variable soil properties. This paper proposes a simple Correlated Sampling Technique (CST) for generating samples of correlated random variables. The CST firstly produces correlated standard-normally distributed samples through linear combinations of independent standard-normally distributed samples. Correlated arbitrarily distributed samples can then be obtained by the Nataf transformation. The CST was combined with FOSM (named CST-based FOSM) for probabilistic slope stability analysis. The slope reliabilities of a single-layered cohesive soil slope and a high earth and rockfill dam were analyzed to illustrate the CST-based FOSM. These illustrative examples indicated that the CST-based FOSM can accurately estimate the slope reliability indices with considerably fewer simulations (especially in the case of low failure probability) compared with direct MCS, and the slope reliability was sensitive to the correlation of the strength parameters. 相似文献
13.
基于模糊随机理论的广义可靠度在边坡稳定性分析中的应用 总被引：4，自引：2，他引：4
在常规的概率极限状态理论基础上 ,分析了边坡体安全状态的模糊性 ,在此基础上构造了基于MCS以及FORM算法的模糊随机可靠度算法 ,分别就边坡体的滑动失效、渗透破坏两种失效模式作了参数敏感性分析 ,并进一步对边坡体的系统失效模式作了探讨 相似文献
14.
This paper develops a multiple response surfaces approach to approximate the limit state function for slope failure by second‐order polynomial functions, to incorporate the variation of the most probable slip surfaces, and to evaluate the slope failure probability pf. The proposed methodology was illustrated through a cohesive soil slope example. It is shown that the pf values estimated from multiple response surfaces agree well with those pf values that have been obtained by searching a large number of potential slip surfaces in each Monte Carlo simulation (MCS) sample. The variation of number of the most probable slip surfaces is studied at different scale of fluctuation (λ) values. It is found that when full correlation assumed for each of random fields (i.e., spatial variability is ignored), the number of the most probable slip surfaces is equal to the number of random fields (in this study, it is 3). When the spatial variability grows significantly, the number of the most probable slip surfaces or number of multiple response surfaces firstly increases evidently to a higher value and then varies slightly. In addition, the contribution of a specific most probable slip surface varies dramatically at different spatial variability level, and therefore, the variation of the most probable slip surfaces should be accounted for in the reliability analysis. The multiple response surfaces approach developed in this paper provides a limit equilibrium method and MCS‐based means to incorporate such a variation of the most probable slip surfaces in slope reliability analysis. Copyright © 2014 John Wiley & Sons, Ltd. 相似文献
15.
A Kriging-based surrogate model provides a logically strict and efficient tool to evaluate the system reliability of a slope. However, the constant trend function adopted in the ordinary Kriging (OK) cannot always well capture the nonlinear non-smooth properties of a slope stability problem. Although the universal Kriging (UK) with a linear or a quadratic trend function could be an alternative for some cases, a higher order nonlinear trend function is preferable for some more complicated nonlinear non-smooth cases in the slope stability analysis. To address this problem, a genetic algorithm (GA) optimized Taylor Kriging (TK) surrogate model is proposed for the system reliability analysis of soil slopes in this paper. The proposed surrogate model allows a unified framework of the Kriging, considering different extents of nonlinear properties according to the Taylor expansion order (e.g., can be as high as the fourth order). The GA is introduced to search for the optimal correlation parameters, of which the effectiveness is verified by an analytical example. The feasibility of the proposed surrogate model is then validated by two analytical examples before its application to the practical slope reliability analyses. The results show that the UK model can be incorporated into the TK model, and the TK model provides a higher accuracy and efficiency when facing the highly nonlinear slope stability problems. It is also found that the UK model cannot fully capture the potential nonlinear properties existed in a slope stability model as compared with the higher order TK model. 相似文献
16.
Xiao-hui Tan Xian-en Wang Xiao-jun Hu Meng-fen Shen Na Hu 《Geotechnical and Geological Engineering》2016,34(5):1283-1292
Two methods of reliability analysis of soil slopes are studied, and the representative flow charts of both methods are illustrated. Method 1 can predict the reliability index and the critical probabilistic slip surface directly and it is computational efficient, but it needs the development of new codes for integrating the reliability analysis code and the slope stability code. Method 2 makes the reliability analysis code call the slope stability analysis code directly, and each code can be considered as an intact part. The main result of Method 2 is the reliability index of soil slope. Combined with the proposed method for locating the critical slip surface, Method 2 can also predict the probabilistic slip surface. Although Method 2 needs much more callings of the subprogram of slope stability analysis code, it needs not the developing of new computer program. Thus, Method 2 is easy to use and can be applied to different reliability analysis methods and slope stability analysis methods. 相似文献
17.
Slope stability analysis is a geotechnical engineering problem characterized by many sources of uncertainty. Some of these sources are connected to the uncertainties of soil properties involved in the analysis. In this paper, a numerical procedure for integrating a commercial finite difference method into a probabilistic analysis of slope stability is presented. Given that the limit state function cannot be expressed in an explicit form, an artificial neural network (ANN)-based response surface is adopted to approximate the limit state function, thereby reducing the number of stability analysis calculations. A trained ANN model is used to calculate the probability of failure through the first- and second-order reliability methods and a Monte Carlo simulation technique. Probabilistic stability assessments for a hypothetical two-layer slope as well as for the Cannon Dam in Missouri, USA are performed to verify the application potential of the proposed method. 相似文献
18.
提出基于非侵入式随机有限元法的边坡可靠度分析方法,并编写计算程序NISFEM。采用有限元滑面应力法计算边坡安全系数,将Hermite随机多项式展开与SIGMA/W和SLOPE/W模块有机结合实现边坡可靠度非侵入式随机分析。根据随机多项式展开系数,给出边坡安全系数前4阶统计矩（均值、标准差、偏度和峰度）和Sobol指标解析表达式,并采用Sobol指标进行边坡可靠度参数敏感性分析。最后,以均质土坡可靠度问题为例,证明该方法在边坡可靠度分析中的有效性。结果表明,边坡可靠度分析的非侵入式随机有限元法能够有效地考虑边坡变形对边坡可靠度的影响,计算效率远远高于蒙特卡罗模拟方法（MCS）,是解决复杂边坡可靠度问题一种有效地分析手段;黏聚力和内摩擦角变异性对边坡安全系数前四阶统计矩具有明显的影响,重度变异性对安全系数前4阶统计矩几乎没有影响;抗剪强度参数间负相关性对边坡安全系数均值几乎没有影响,但对安全系数标准差、偏度和峰度均有明显的影响。此外,随着抗剪强度参数间负相关性的增加,边坡安全系数由近似正态分布逐渐变为明显的非正态分布。 相似文献
19.
边坡稳定性与其影响因素之间存在着复杂的非线性关系。通过分析影响边坡稳定性的主要因素,采用支持向量机建立边坡稳定性和影响因素之间的非线性关系;同时,考虑到支持向量机参数对预测效果的影响,采用连续蚁群算法对其进行优化选择,从而提出边坡稳定性预测的蚁群优化支持向量机模型。锦屏一级右岸拱肩槽部位谷坡为顺向坡,绝大部分基岩裸露,自然边坡为大理岩边坡,现状稳定。结合锦屏一级右岸拱肩槽边坡,采用蚁群优化支持向量机模型对其稳定性进行预测分析,预测结果与实际情况吻合较好,说明蚁群优化支持向量机模型在边坡稳定性分析中具有良好的实际应用价值。 相似文献
20.
《Computers and Geotechnics》2006,33(4-5):260-274
Three-dimensional (3D) evaluation of slope stability is a widely addressed problem in the domain of geotechnical engineering. The growing popularity of the geographical information system (GIS) approach, with capacities ranging from conventional data storage to complex spatial analysis and graphical presentation, means that it is also becoming a powerful tool for geotechnical engineers. In this study, in which we combine GIS grid-based data with four proposed column-based models of 3D slope stability analysis, we have devised new correspondent GIS grid-based 3D deterministic models to calculate the safety factor of the slope. Based on the four GIS-based 3D slope stability analysis models, a GIS-based program, 3DSlopeGIS, has been developed to implement the algorithm where all the input data are in the same format as the GIS dataset. The 3DSlopeGIS system, which is an extension of the widely used GIS software package, represents the combined development of 3D slope stability analysis and GIS-based component object model (COM) skills. Since all related data are supplied in the GIS format, this new database approach will be convenient for the repeated renewal and consulting of data. Certain widely addressed examples are evaluated in this paper and the results show the correction and potential of this GIS-based tool as a means of assessing the 3D stability of a slope. Two practical slope problems have been evaluated using the 3DSlopeGIS system. The results illustrate the convenience of data management as well as the effective range selection of Monte-Carlo random variables and the critical slip surface location in some parts of a lava dome. 相似文献