共查询到20条相似文献，搜索用时 779 毫秒
1.
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
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.
A review of probabilistic and deterministic liquefaction evaluation procedures reveals that there is a need for a comprehensive approach that accounts for different sources of uncertainty in liquefaction evaluations. For the same set of input parameters, different models provide different factors of safety and/or probabilities of liquefaction. To account for the different uncertainties, including both the model and measurement uncertainties, reliability analysis is necessary. This paper presents a review and comparative study of such reliability approaches that can be used to obtain the probability of liquefaction and the corresponding factor of safety. Using a simplified deterministic Seed method, this reliability analysis has been performed. The probability of liquefaction along with the corresponding factor of safety have been determined based on a first order second moment (FOSM) method, an advanced FOSM (Hasofer–Lind) reliability method, a point estimation method (PEM) and a Monte Carlo simulation (MCS) method. A combined method that uses both FOSM and PEM is presented and found to be simple and reliable for liquefaction analysis. Based on the FOSM reliability approach, the minimum safety factor value to be adopted for soil liquefaction analysis (depending on the variability of soil resistance, shear stress parameters and acceptable risk) has been studied and a new design safety factor based on a reliability approach is proposed. 相似文献
4.
基于PSO和LSSVM的边坡稳定性评价方法 总被引：5，自引：0，他引：5
提出了基于粒子群算法（PSO）和最小二乘支持向量机（LSSVM）的边坡稳定性评价方法。该模型既利用了最小二乘支持向量机求解速度快、易于描述非线性关系的优良特性,同时也利用了粒子群算法快速全局优化的特点。粒子群算法用于搜索最小二乘支持向量机模型的最优参数,然后将模型用于预测边坡的安全系数。计算结果表明,该方法是合理的、有效的。 相似文献
5.
确定性设计安全系数法由于简单易行得到了广泛的应用。可靠度方法在一定程度上弥补了确定性设计中不能考虑实际工程中岩体参数离散性的问题，使设计更加符合实际情况。函数连分式方法在边坡一次二阶矩概率设计中可以方便地计算出状态函数对各随机变量的偏导数，也可使传统安全系数法和可靠度分析有机结合。考虑经济最优的原则，结合风险分析来确定可靠度法设计中的可靠度数值问题。由安全系数和可靠性共同度量边坡系统的稳定性，建立传统安全系数与可靠度理论耦合的边坡稳定二元体系，可有效地考虑边坡系统内实际存在的不确定性和相关性，使边坡的稳定性评价更加客观精确，为边坡安全和滑坡灾害的风险性管理提供理论和方法。 相似文献
6.
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
7.
LHS方法在边坡可靠度分析中的应用 总被引：8，自引：0，他引：8
Monte Carlo（MC）法在目前边坡可靠度分析中是一种相对精确的方法,应用广泛,受问题限制的影响较小,适应性很强,其误差仅与标准差和样本容量有关。但其精度受随机抽样的可靠性和模拟次数制约,收敛速度慢,影响了实际使用。在极限平衡方法的基础上,用拉丁超立方抽样（Latin hypercube sampling,LHS）方法代替MC法的随机抽样,考虑边坡参数的变异性和相关性进行边坡可靠度分析。讨论了LHS法、MC法中可靠指标的各种计算方法,建议以破坏概率、安全系数均值和标准差作为评价指标。算例显示LHS法较MC法效率上有很大改善：较少的抽样样本就能反映参数的概率分布,可靠度分析收敛快,不需要大量的模拟,因此,值得在边坡可靠度分析中推广应用。也将工程上常用的均匀设计和正交设计用于边坡可靠度分析,结果表明,正交设计结果和中心点法比较接近,而均匀设计得到的结果则是不可靠的。 相似文献
8.
This paper examines the potential of least‐square support vector machine (LSVVM) in the prediction of settlement of shallow foundation on cohesionless soil. In LSSVM, Vapnik's ε‐insensitive loss function has been replaced by a cost function that corresponds to a form of ridge regression. The LSSVM involves equality instead of inequality constraints and works with a least‐squares cost function. The five input variables used for the LSSVM for the prediction of settlement are footing width (B), footing length (L), footing net applied pressure (P), average standard penetration test value (N) and footing embedment depth (d). Comparison between LSSVM and some of the traditional interpretation methods are also presented. LSSVM has been used to compute error bar. The results presented in this paper clearly highlight that the LSSVM is a robust tool for prediction of settlement of shallow foundation on cohesionless soil. Copyright © 2008 John Wiley & Sons, Ltd. 相似文献
9.
Pijush Samui Pradeep Kurup S. Dhivya J. Jagan 《Geotechnical and Geological Engineering》2016,34(2):579-584
First order reliability method (FORM) is generally used for reliability analysis in geotechnical engineering. This article adopts generalized regression neural network (GRNN) based FORM, Gaussian process regression (GPR) based FORM and multivariate adaptive regression spline (MARS) based FORM for reliability analysis of quick sand condition. GRNN is related to the radial basis function (RBF) network. GPR is developed based on probabilistic framework. MARS is a nonparametric regression technique. A comparative study has been carried out between the developed models. The performance of GPR based FORM and MARS based FORM match well with the FORM. This article gives the alternative methods for reliability analysis of quick sand condition. 相似文献
10.
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。 相似文献
11.
基于认知聚类分区方法的边坡可靠度分析 总被引：1，自引：0，他引：1
提出了边坡可靠度分析的一种新的全局优化方法--认知聚类分区方法。该方法主要包括5个步骤：分区、随机抽样、计算极径L、回代、计算可靠指标及验算点。给出了相应的计算流程图,并编写了基于C语言的计算程序KCPREL。最后,以岩质边坡稳定可靠度问题为例证明了所提方法的有效性。结果表明,认知聚类分区方法能同时计算出可靠指标和验算点,并能获得全局最优解。该方法的计算精度和蒙特卡洛模拟方法相当,计算效率远远高于传统的蒙特卡洛模拟方法。此外,该方法在分析含有复杂的隐式及非线性功能函数的边坡稳定可靠度问题方面体现出明显的优越性。等步长认知聚类分区方法能全面且均匀地搜索角度,从而得到更准确的验算点。为了保证足够的计算精度及减小计算量,建议步长取10º以内。 相似文献
12.
在统计矩点估计法和模糊随机理论的基础之上,提出边坡工程模糊随机可靠性分析的模糊点估计法,将边坡稳定性极限状态方程由模糊随机集向普通随机集转化,然后利用点估计法求解边坡的可靠度指标。鉴于岩土体物理力学参数的近似分布类型,采用区别于梯形模糊数的正态模糊数对随机变量进行模糊随机化处理,使其更趋近于物理力学参数的近似分布类型。该方法考虑了各个力学参数的模糊性,计算结果更能反映边坡的真实工作状态。算例分析结果表明,该法使用简便,计算效率高,结果可靠,避免了传统分析方法的缺点,对复杂边坡或者功能函数为隐式表达的边坡工程可靠性分析具有很大的潜力,为边坡可靠性分析提供了一条新的途径,具有广泛的应用前景。 相似文献
13.
14.
The methods used in conducting static stability analyses have remained pertinent to this day for reasons of both simplicity and speed of execution. The most well‐known of these methods for purposes of stability analysis of fractured rock masses is the key‐block method (KBM). This paper proposes an extension to the KBM, called the ‘key‐group method’ (KGM), which combines not only individual key‐blocks but also groups of collapsable blocks into an iterative and progressive analysis of the stability of discontinuous rock slopes. To take intra‐group forces into account, the Sarma method has been implemented within the KGM in order to generate a Sarma‐based KGM, abbreviated ‘SKGM’. We will discuss herein the hypothesis behind this new method, details regarding its implementation, and validation through comparison with results obtained from the distinct element method. Furthermore, as an alternative to deterministic methods, reliability analyses or probabilistic analyses have been proposed to take account of the uncertainty in analytical parameters and models. The FOSM and ASM probabilistic methods could be implemented within the KGM and SKGM framework in order to take account of the uncertainty due to physical and mechanical data (density, cohesion and angle of friction). We will then show how such reliability analyses can be introduced into SKGM to give rise to the probabilistic SKGM (PSKGM) and how it can be used for rock slope reliability analyses. Copyright © 2005 John Wiley & Sons, Ltd. 相似文献
15.
基于灰色最小二乘支持向量机的边坡位移预测 总被引：1，自引：0，他引：1
利用边坡实测位移序列预测边坡未来时间的位移,可以有效地判断边坡的稳定性。在分析了灰色预测方法和最小二乘支持向量机各自的优缺点的基础上,提出了将二者相结合的一种新的预测模型--灰色最小二乘支持向量机预测模型。新模型既发挥了灰色预测方法中“累加生成”的优点,弱化了原始序列中随机扰动因素的影响,增强了数据的规律性,又充分利用了最小二乘支持向量机求解速度快、易于描述非线性关系的优良特性,避免了灰色预测方法及模型存在的理论缺陷。同时,采用遗传算法进行了模型的参数优化,通过2个工程实例说明灰色最小二乘支持向量机模型预测边坡位移的有效性,具有较高的精度。 相似文献
16.
极限平衡分析方法是斜坡稳定性评价中的常用方法,在长期的工程实践中积累了丰富的经验,但其不能考虑斜坡岩土体中实际存在的不确定性,在应用中具有一定的局限性。可靠度分析方法可有效地考虑斜坡系统内的不确定性和相关性,但因状态函数偏导数的求解比较困难,使可靠度分析方法在实际中应用不便。为解决上述问题,根据二元函数插值逼近原理,在矩形区域上构造拉格朗日不完全双二次多项式逼近状态函数,从而近似地计算状态函数的偏导数,求得状态函数的均值和方差,并利用精度较高的一次二阶矩方法来计算斜坡的可靠指标和破坏概率。据鄂西恩施地区马堡营滑坡实例分析表明,引入二元函数插值逼近的一次二阶矩方法计算结果与剩余推力法及Monte-carlo模拟方法结果一致,其精度可满足工程需求。 相似文献
17.
A new computing method is proposed for reliable analysis. The limit state function is implicit and nonlinear in reliability analysis of slopes and is difficult to apply by traditional reliability methods, especially in large‐scale project applications. Relevance vector machines (RVMs) are capable of approximating the limit state function without the need for additional assumptions regarding the function form, as opposed to traditional polynomial response surfaces. RVMs were adapted to obtain the limit state function. We propose an RVM‐based response surface method combined with the first‐order reliability method for slope reliability analysis and describe its step‐by‐step implementation. The reliability index obtained from the proposed method shows excellent agreement with traditional response surface method results. Copyright © 2017 John Wiley & Sons, Ltd. 相似文献
18.
岩土工程可靠度分析中,功能函数往往呈隐式且具有强非线性性质,而目前最为实用的矩方法,如JC法、二次二阶矩法,主要适用于显式功能函数情形。为此,将高效的统计矩估计方法和可靠度分析的高阶矩法相结合,提出了一种岩土工程可靠度分析的改进四阶矩方法。首先,通过引入变量的独立化变换和线性变换将功能函数转换为参考变量的函数,并结合多变量函数的单变量降维近似方法和参考变量计算节点与权系数的确定方法,建立了功能函数前四阶矩的高效点估计法。然后,将上述统计矩与立方正态变换假设相结合,提出了易于实现的岩土工程可靠度分析的改进四阶矩方法。最后,由数学算例验证了统计矩估计方法的效率和精度,并通过经典的岩土工程算例验证了建议的改进四阶矩方法具有高效率、高精度且操作简单等特点。 相似文献
19.
《地学前缘(英文版)》2016,7(1)
This article adopts three artificial intelligence techniques, Gaussian Process Regression(GPR), Least Square Support Vector Machine(LSSVM) and Extreme Learning Machine(ELM), for prediction of rock depth(d) at any point in Chennai. GPR, ELM and LSSVM have been used as regression techniques.Latitude and longitude are also adopted as inputs of the GPR, ELM and LSSVM models. The performance of the ELM, GPR and LSSVM models has been compared. The developed ELM, GPR and LSSVM models produce spatial variability of rock depth and offer robust models for the prediction of rock depth. 相似文献
20.
Least square support vector machine and relevance vector machine for evaluating seismic liquefaction potential using SPT 总被引：4，自引：1，他引：3
Pijush Samui 《Natural Hazards》2011,59(2):811-822
The determination of liquefaction potential of soil is an imperative task in earthquake geotechnical engineering. The current
research aims at proposing least square support vector machine (LSSVM) and relevance vector machine (RVM) as novel classification
techniques for the determination of liquefaction potential of soil from actual standard penetration test (SPT) data. The LSSVM
is a statistical learning method that has a self-contained basis of statistical learning theory and excellent learning performance.
RVM is based on a Bayesian formulation. It can generalize well and provide inferences at low computational cost. Both models
give probabilistic output. A comparative study has been also done between developed two models and artificial neural network
model. The study shows that RVM is the best model for the prediction of liquefaction potential of soil is based on SPT data. 相似文献