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1.
《岩土力学》2017,(12):3555-3564
某一特定岩土场地的试验数据、监测资料和观测信息等通常十分有限,然而贝叶斯方法却可充分利用有限的场地信息克服试验数据样本量较小的不足。为有效估计有限样本条件下参数统计特征,提出了基于结构可靠度方法和贝叶斯更新(BUS)的边坡可靠度更新方法,通过融入直剪试验数据更新无限长边坡可靠度验证了提出方法的有效性,并系统探讨了岩土体参数先验信息如试验样本量、概率分布和似然函数模型对边坡可靠度更新的影响规律。结果表明:BUS方法能够考虑岩土体参数概率分布和似然函数模型的影响,融入有限的场地信息准确地估计参数统计特征和更新边坡可靠度,为解决有限样本条件下边坡可靠度更新问题提供了一条有效的途径。土体参数概率分布对边坡可靠度更新结果(参数后验均值、标准差以及更新的失效概率)具有重要的影响,基于常用的正态和对数正态分布的边坡可靠度更新结果偏于保守,相比之下,似然函数模型对边坡可靠度更新结果的影响相对较小。此外,岩土体参数不确定性和更新的边坡失效概率均随着试验样本量的增大而减小,但当样本量增大到一定程度时它们的变化不大。 相似文献
2.
概率反分析是推断不确定土体参数统计特征的重要手段,可以使边坡可靠度评估更接近工程实际。然而目前的概率反分析很少使用多源信息（包括监测数据、观测信息和边坡服役记录）,因为这通常涉及数千个随机变量和高维似然函数的评估。因此融合多源信息对空间变异土体参数进行概率反分析进而预测降雨条件下的边坡可靠度是一项具有挑战性的难题。文章将改进的基于子集模拟的贝叶斯更新（mBUS）方法与自适应条件抽样（aCS）算法相结合,构建了空间变异土体参数概率反分析和边坡可靠度预测的框架,并以某一公路边坡为例验证了该框架的有效性。研究结果表明：通过融合多源信息所获得的土体参数后验统计特征与现场观测结果基本吻合;用更新后的土体参数预测得到2004年9月12日该边坡在暴雨工况下的失效概率为23.1%,符合实际边坡失稳情况,说明在此框架下可以充分利用多源信息解决高维概率反分析问题。 相似文献
3.
边坡可靠度分析中通常假定采用平稳或准平稳随机场表征土体参数的空间变异性,然而大量现场试验数据表明,土体参数如不排水抗剪强度沿土体埋深常呈现明显的非平稳分布特征,即其均值和标准差均随埋深发生变化,因此亟需发展土体参数非平稳随机场模型及其模拟方法。针对目前不能有效单独模拟土体参数趋势分量和随机波动分量的不确定性,提出了一种有效的不排水抗剪强度参数非平稳随机场模型,并给出了土体参数二维非平稳随机场模拟方法计算流程,同时将新提出的模型与现有非平稳随机场模型及平稳随机场模型进行了系统比较。最后通过不排水饱和黏土边坡算例验证了提出模型的有效性,并揭示了不排水抗剪强度非平稳分布特征对边坡可靠度的影响规律。结果表明：提出模型能够有效地单独模拟土体参数趋势分量和随机波动分量的不确定性,考虑土体参数均值和标准差随埋深增加而增大的特性,可为表征土体参数非平稳分布特征提供了一条有效的途径。此外,与采用非平稳随机场模拟土体参数空间变异性相比,采用常用的平稳随机场模型会低估边坡失效概率,从而造成偏危险的边坡工程设计方案。 相似文献
4.
受工程勘察成本及试验场地限制,可获得的试验数据通常有限,基于有限的试验数据难以准确估计岩土参数统计特征和边坡可靠度。贝叶斯方法可以融合有限的场地信息降低对岩土参数不确定性的估计进而提高边坡可靠度水平。但是,目前的贝叶斯更新研究大多假定参数先验概率分布为正态、对数正态和均匀分布,似然函数为多维正态分布,这种做法的合理性有待进一步验证。总结了岩土工程贝叶斯分析常用的参数先验概率分布及似然函数模型,以一个不排水黏土边坡为例,采用自适应贝叶斯更新方法系统探讨了参数先验概率分布和似然函数对空间变异边坡参数后验概率分布推断及可靠度更新的影响。计算结果表明：参数先验概率分布对空间变异边坡参数后验概率分布推断及可靠度更新均有一定的影响,选用对数正态和极值I型分布作为先验概率分布推断的参数后验概率分布离散性较小。选用Beta分布和极值I型分布获得的边坡可靠度计算结果分别偏于保守和危险,选用对数正态分布获得的边坡可靠度计算结果居中。相比之下,似然函数的影响更加显著。与其他类型似然函数相比,由多维联合正态分布构建的似然函数可在降低对岩土参数不确定性估计的同时,获得与场地信息更为吻合的计算结果。另外,构建似然函数时不同位置处测量误差之间的自相关性对边坡后验失效概率也具有一定的影响。 相似文献
5.
约束随机场下的边坡可靠度随机有限元分析方法 总被引：2，自引：1，他引：1
目前边坡可靠度中常用的简化分析方法,不考虑边坡土体的空间变异性,每次计算整个边坡都取用相同的强度参数,由离散点试样试验得到的土体参数统计特性只能反映点特性,而边坡的稳定性受滑面上平均抗剪强度特性控制,因此,需要考虑空间范围内的平均特性。描述空间变异性的随机场理论对变异性较高的土体,实际上高估了其空间变异性。把随机场理论和地质统计中的区域化变量理论结合起来,建立约束随机场,并在此基础上进行Monte-Carlo随机有限元分析。计算实例表明,在高变异性条件下约束随机场能有效降低完全随机场的模拟方差,得到更低的破坏概率。对比了随机有限元和简化法的计算结果表明,简化法在土体强度变异性很高时其结果并非偏于保守。另外也指出了可靠度分析中存在的边坡尺度效应和简化法的适用条件。 相似文献
6.
地层不确定性显著影响浅基础承载力，其不确定性主要包括地层变异和参数空间变异性。以往研究已分别研究了地层变异和参数空间变异性对浅基础承载力的影响。旨在建立浅基础承载力的随机概率计算框架，以揭示地层不确定性对浅基础承载能力的影响。其中，使用马尔可夫随机场模拟地层变异。在此基础上，考虑土体参数竖向相关距离的变化，使用对数正态随机场模拟不同地层中土体参数的空间变异性。基于深圳妈湾的钻孔和土体参数数据，根据所提出的计算框架进行浅基础承载力分析。使用子集模拟方法加速计算各方案的可靠度，提出了缩小系数对计算结果进行不同程度的缩减以达到简化考虑空间变异性的效果。定义了贡献率指标以定量计算地层变异和参数空间变异性对浅基础承载力计算结果的影响。结果表明，如果不考虑地层不确定性，传统确定性的承载力计算会高估浅基础的承载力。当钻孔数量较少时，地层变异对承载力计算起主要影响；当钻孔数量充足时，则由参数空间变异主导。 相似文献
7.
考虑参数空间变异性的非饱和土坡可靠度分析 总被引：2，自引：0，他引：2
在考虑多个土体参数空间变异性的基础上,提出了基于拉丁超立方抽样的非饱和土坡稳定可靠度分析的非侵入式随机有限元法。利用Hermite随机多项式展开拟合边坡安全系数与输入参数间的隐式函数关系,采用拉丁超立方抽样技术产生输入参数样本点,通过Karhunen-Loève展开方法离散土体渗透系数、有效黏聚力和内摩擦角随机场,并编写了计算程序NISFEM-KL-LHS。研究了该方法在稳定渗流条件下非饱和土坡可靠度分析中的应用。结果表明：非侵入式随机有限元法为考虑多个土体参数空间变异性的非饱和土坡可靠度问题提供了一种有效的分析工具。土体渗透系数空间变异性和坡面降雨强度对边坡地下水位和最危险滑动面位置均有明显的影响。当降雨强度与饱和渗透系数的比值大于0.01时,边坡失效概率急剧增加。当土体参数变异性或者参数间负相关性较大时,忽略土体参数空间变异性会明显高估边坡失效概率。 相似文献
8.
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
9.
基于Bootstrap抽样技术提出了有限数据条件下边坡可靠度分析方法。简要介绍了传统的边坡可靠度分析方法。采用Bootstrap方法模拟了抗剪强度参数概率分布函数的统计不确定性。以无限边坡为例研究了抗剪强度分布参数和分布类型不确定性对边坡可靠度的影响规律。结果表明：基于有限数据估计的样本均值、样本标准差和AIC值具有较大的变异性,这种变异性进一步导致了抗剪强度参数概率分布函数存在明显的统计不确定性。在考虑抗剪强度参数概率分布函数的统计不确定性时,边坡可靠度指标应为具有一定置信度水平的置信区间,而不是传统可靠度分析中的固定值。边坡可靠度指标的置信区间变化范围随安全系数的增加而增大,同时考虑分布参数和分布类型不确定性计算的可靠度指标具有更大的变异性和更宽的置信区间变化范围。Bootstrap方法为有限数据条件下抗剪强度参数概率分布函数统计不确定性的模拟以及边坡可靠度的评估提供了一条有效的途径。 相似文献
10.
《岩土力学》2017,(5):1385-1396
目前涉及岩土体不均匀性的岩土问题可靠度分析大多只考虑岩土体的固有变异,即岩土体特性参数的不均匀性,未考虑涉及不同岩土体材料的地层变异。地层变异在实际滑坡中广泛存在,其表现为不同类型岩土材料(如黏土、粉土、砂土)的互相嵌套,或一种类型岩土体在另一种较均质岩土体中的随机分布。为此,提出了利用钻孔资料评估考虑地层变异时边坡稳定不确定性的分析方法。根据已有钻孔资料设计了不同的钻孔布置方案,建立了表征地层变异的马尔可夫链模型,采用有限元强度折减法进行边坡稳定性分析,探讨了钻孔布置方案对评估边坡稳定安全系数和失效概率不确定性的影响。以澳大利亚珀斯市钻孔资料为例,分析了所提方法的有效性。结果表明,钻孔布置方案对边坡稳定安全系数和失效概率的不确定性评估有重要影响。考虑地层变异时,边坡稳定安全系数可以用Johnson分布来描述;边坡稳定安全系数统计量和失效概率不一定随钻孔数量增加而单调变化,边坡影响区域以内钻孔对评估边坡稳定安全系数的不确定性最为有效;当钻孔数目逐渐增加时,边坡安全系数均值逐渐收敛至精确值。 相似文献
11.
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
12.
Te Xiao Zi-Jun Cao Xiao-Song Tang 《Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards》2017,11(1):146-159
ABSTRACTA simplified reliability analysis method is proposed for efficient full probabilistic design of soil slopes in spatially variable soils. The soil slope is viewed as a series system comprised of numerous potential slip surfaces and the spatial variability of soil properties is modelled by the spatial averaging technique along potential slip surfaces. The proposed approach not only provides sufficiently accurate reliability estimates of slope stability, but also significantly improves the computational efficiency of soil slope design in comparison with simulation-based full probabilistic design. It is found that the spatial variability has considerable effects on the optimal slope design. 相似文献
13.
Embankment slopes composed of spatially variable soils have a variety of different failure modes that are affected by the correlation distances of the material properties and the geometry and total length of the slope. This paper examines the reliability of soil slopes for embankments of different length and uses parallel computing to analyse very long embankments (up to 100 times the embankment height) for a clay soil characterised by a spatially varying undrained shear strength. Based on a series of analyses using the 3D random finite element method (RFEM), it is first shown that the reliability of slopes of various length can be efficiently computed by combining simple probability theory with a detailed 3D RFEM analysis of a representative shorter slope of length 10 times the slope height. RFEM predictions of reliability indices for longer slopes are then compared with results obtained using Vanmarcke's (1977a) simplified 3D method and Calle's (1985) extended 2D approach. It is shown that these methods can give significantly different results, depending on the horizontal scale of fluctuation relative to the slope length, with RFEM predicting a lower slope reliability than the Vanmarcke and Calle solutions in all cases. The differences in the solutions are evaluated and attributed to differences in the assumed and computed failure surface geometries. 相似文献
14.
Discarding known data from cored samples in the reliability analysis of a slope in spatially variable soils is a waste of site investigation effort. The traditional unconditional random field simulation, which neglects these known data, may overestimate the simulation variance of the underlying random fields of the soil properties. This paper attempts to evaluate the reliability of a slope in spatially variable soils while considering the known data at particular locations. Conditional random fields are simulated based on the Kriging method and the Cholesky decomposition technique to match the known data at measured locations. Subset simulation (SS) is then performed to calculate the probability of slope failure. A hypothetical homogeneous cohesion-frictional slope is taken as an example to investigate its reliability conditioned on several virtual samples. Various parametric studies are performed to explore the effect of different layouts of the virtual samples on the factor of safety (FS), the spatial variation of the critical slip surface and the probability of slope failure. The results suggest that whether the conditional random fields can be accurately simulated depends highly on the ratio of the sample distance and the autocorrelation distance. Better simulation results are obtained with smaller ratios. Additionally, compared with unconditional random field simulations, conditional random field simulations can significantly reduce the simulation variance, which leads to a narrower variation range of the FS and its location and a much lower probability of failure. The results also highlight the great significance of the conditional random field simulation at relatively large autocorrelation distances. 相似文献
15.
Reza Jamshidi Chenari Reza Alaie 《Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards》2015,9(2):109-123
Slopes are mainly naturally occurred deposits, so slope stability is highly affected by inherent uncertainty. In this paper, the influence of heterogeneity of undrained shear strength on the performance of a clay slope is investigated. A numerical procedure for a probabilistic slope stability analysis based on a Monte Carlo simulation that considers the spatial variability of the soil properties is presented to assess the influence of randomly distributed undrained shear strength and to compute reliability as a function of safety factor. In the proposed method, commercially available finite difference numerical code FLAC 5.0 is merged with random field theory. The results obtained in this study are useful to understand the effect of undrained shear strength variations in slope stability analysis under different slope conditions and material properties. Coefficient of variation and heterogeneity anisotropy of undrained shear strength were proven to have significant effect on the reliability of safety factor calculations. However, it is shown that anisotropy of the heterogeneity has a dual effect on reliability index depending on the level of safety factor adopted. 相似文献
16.
《地学前缘(英文版)》2018,9(6):1657-1664
A long slope consisting of spatially random soils is a common geographical feature. This paper examined the necessity of three-dimensional(3 D) analysis when dealing with slope with full randomness in soil properties. Although 3 D random finite element analysis can well reflect the spatial variability of soil properties, it is often time-consuming for probabilistic stability analysis. For this reason, we also examined the least advantageous(or most pessimistic) cross-section of the studied slope. The concept of"most pessimistic" refers to the minimal cross-sectional average of undrained shear strength. The selection of the most pessimistic section is achievable by simulating the undrained shear strength as a 3 D random field. Random finite element analysis results suggest that two-dimensional(2 D) plane strain analysis based the most pessimistic cross-section generally provides a more conservative result than the corresponding full 3 D analysis. The level of conservativeness is around 15% on average. This result may have engineering implications for slope design where computationally tractable 2 D analyses based on the procedure proposed in this study could ensure conservative results. 相似文献
17.
Jing-Sen Cai Tian-Chyi Jim Yeh E-Chuan Yan Rui-Xuan Tang Jet-Chau Wen Shao-Yang Huang 《Landslides》2018,15(6):1193-1204
An adaptive sampling approach is proposed, which can sample spatially varying shear strength parameters efficiently to reduce uncertainty in the slope stability analysis. This approach employs a limit equilibrium model and stochastic conditional methodology to determine the likely sampling locations. Karhunen-Loève expansion is used to conduct the conditional Monte Carlo simulation. A first-order analysis is also proposed to ease the computational burden associated with Monte Carlo simulation. These approaches are then tested using borehole data from a field site. Results indicate that the proposed adaptive sampling approach is an effective and efficient sampling scheme for reducing uncertainty in slope stability analysis. 相似文献