مقاله انگلیسی رایگان در مورد آنالیز ثبات احتمالی سد خاکی با روش المان محدود – الزویر 2018

The probabilistic analysis of slope stability is a topic that has been widely studied in the literature. Works in this domain have mainly considered uncertainties relating to soil mechanical properties obtained from geotechnical investigations. These uncertainties essentially stem from inherent spatial variability and measurement errors [1,2], to which can be added limited site investigation data and the assumptions included in the stability model [3]. Among these sources of uncertainties, some studies have shown that spatial variability is the most important [4–6]. Therefore, it is the aspect on which research studies have focused most [2,3,7–16]. Thus, the theory of random fields [17] is best suited for modeling the spatial variability of properties of continuous media like soils. In recent years, much research has integrated this kind of modeling in embankment slope stability analysis covering different aspects. For example, Gaouar et al. [18] and Auvinet and Gonzalez [19] studied the reliability of hypothetical earth dams using random fields of undrained shear strength. Nishimura et al. [47] employed the Swedish weight sounding test to identify the spatial correlation structure of the materials of an earth-fill dam. Zheng et al. [48] conducted a Bayesian updating approach with monitoring data to improve the predictions of embankment settlements. Cho [11] used direct MCS to investigate the effect of the spatial variability of shear strength parameters on the failure surface with 1-D random fields and LEM. Others have paid attention to the influence on both seepage and slope stability of the characteristics of random fields like: (i) correlation length [2,20]; (ii) the coefficient of variation (CoV) [14]; (iii) the choice of the autocorrelation function (ACF) used to represent spatial variability [3,16]. Finally, Liu et al. [3] proposed a numerical procedure to combine Subset Simulations (SS) with the Kriging method for assessing the reliability of an embankment slope in spatially variable soils. Most of these studies used the limit equilibrium method (LEM) to assess slope stability. However, with the rapid development of computational tools, FEM is increasingly used in geotechnics. This is particularly true concerning sliding stability analyzes [21] which have been extensively combined with the strength reduction method (SRM), based on Zienkiewicz’s work [22]. SRM has since been used for slope stability analyzes by many authors like Matsui and San [23], Griffiths and Lane [24], Cheng et al. [25], Huang and Jia [26] and others. Combined with SRM technique, FEM is used as an alternative to LEM because it presents several advantages that are described in detail in [24,25]: (i) no assumptions are needed concerning the failure surface, (ii) no assumptions on inter-slice side forces are needed, since there is no concept of slices, and (iii) soil behavior can be modeled in terms of stresses/strains. Furthermore, several studies have shown that LEM and SRM give similar FoS [24,25], except in some specific cases pointed out by Cheng et al. [25]. Different methods are used in the literature to evaluate reliability in geotechnical issues. Some authors used first-order, second-moment (FOSM) methods [2,7,27], whereas others used the FORM method [11,20,28]. MCS is nevertheless the method used most for probabilistic slope stability analysis [3,8,9,13,15,16]. MCS is a simple and robust tool for simulating the statistical distribution of FoS, although it requires considerable computational effort in terms of calculation time [13,15]. Regarding earthfill dams, large quantities of geotechnical data are available in the form of design studies, construction controls and monitoring measurements [29,30]. These data are still not well exploited in studies relating to the reliability of earth dams (or for that of concrete dams [33]), although some authors have underlined the benefits of carrying out a geostatistical analysis on them [31,32]. However, the probabilistic assessment of geotechnical properties generally depends on the type of soils and considerations found in the literature, such as in the work of Phoon and Kulhawy [5,6].