共查询到17条相似文献，搜索用时 234 毫秒
1.
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
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岩土工程现场勘察试验通常只能获得有限的试验数据,据此难以真实地量化土体参数的空间变异性。提出了考虑土体参数空间变异性的概率反演和边坡可靠度更新方法,基于室内和现场两种不同来源的试验数据概率反演空间变异参数统计特征和更新边坡可靠度水平,并给出了计算流程。此外为合理地描述土体参数先验信息,发展了不排水抗剪强度非平稳随机场模型。最后通过不排水饱和黏土边坡算例验证了提出方法的有效性,并探讨了试验数据和钻孔位置对边坡后验失效概率的影响。结果表明：提出方法实现了空间变异土体参数概率反演与边坡可靠度更新的一体化,基于有限的多源试验数据概率反演得到的土体参数均值与试验数据非常吻合,明显降低了对参数不确定性的估计,更新的边坡可靠度水平显著增加。受土体参数空间自相关性的影响,试验数据对钻孔取样点附近区域土体参数统计特征更新的影响明显大于距离取样点较远区域。 相似文献
3.
土坡渐进破坏的可靠度 总被引：3，自引：0，他引：3
在考虑土的抗剪强度参数的变异性及相关性的基础上,以可靠度的方法研究土坡渐进破坏问题。将滑动面上的土体按等弧长划分为n个土条,假设每个土条的安全余量服从正态分布,相邻两个土条的安全余量则服从二元正态分布,根据土条破坏传播的概率,计算土坡渐进破坏的概率以及土坡中裂缝开展长度的期望值, 并对算例的结果进行了讨论。 相似文献
4.
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
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6.
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
7.
《岩土力学》2016,(1)
空间变异土坡可靠度问题应该视为系统可靠度问题,多重响应面法为高效、准确地对其进行求解提供了一条有效途径。针对单一滑面确定了合理的空间变异土坡安全系数的响应面形式,并探讨了可靠度分析精度和随机场离散精度间的近似线性关系。建立基于大量潜在滑面的多重响应面,计算系统失效概率,并识别其中的代表性滑面。通过两个土坡算例验证所提方法的有效性。结果表明:随着空间变异性的增强,土坡可靠度的系统性增强,单一滑面的失效概率将显著低估土坡整体失效概率;通过控制随机场离散精度,可以事先保证一定的可靠度分析精度,从而有效地避免离散出过多并不重要的随机变量;合理地选择多重响应面形式有利于进一步提高计算效率和计算精度;多重响应面法可以同时分析所有潜在滑面的失效概率以及系统失效概率,并识别出代表性滑面,为边坡防治提供参考。 相似文献
8.
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
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《岩土力学》2017,(2)
常用的计算失效模式间近似相关系数存在一定的误差,采用Pearson相关系数准确地表征边坡失效模式间相关性。基于近似相关系数和Pearson相关系数,研究了土体参数空间变异性对边坡失效模式间相关性、代表性失效模式数目、边坡系统失效概率上、下限3方面的影响。简要介绍了选取边坡代表性滑动面的风险聚类法以及系统失效概率上、下限的Ditlevsen双模界限公式。以单层和两层边坡为例研究了近似相关系数的适用性。结果表明:常用的近似相关系数不能考虑土体参数空间变异性对边坡失效模式间相关性的影响,而Pearson相关系数能够有效地反映土体参数空间变异性对边坡失效模式间相关性的影响。当土体参数空间变异性较弱时,近似相关系数与Pearson相关系数间差别明显,基于近似相关系数会选取过多的代表性滑动面,不能有效地反映边坡代表性破坏模式。此外,基于近似相关系数计算的边坡系统失效概率上限会超过1,系统失效概率上、下限范围很宽,使得系统失效概率上、下限失去了意义。相比之下,基于Pearson相关系数计算的边坡系统失效概率上、下限范围较窄,能够有效地反映系统失效概率变化情况。 相似文献
10.
针对现有加筋土坡稳定性分析研究大多基于线性Mohr-Coulomb破坏准则的现状,本文考虑了岩土材料破坏的非线性特性,采用非线性破坏准则和外切直线法,引入极限分析上限理论进行研究。根据加筋土坡的工程特性和变形机理,考虑破坏滑动层上筋材与土体变形协调特点及速度变化的连续性,分开计算素土的内力功和筋材能量耗散功率,在此基础上,建立直线破裂面和对数螺线破裂面机构的加筋土坡临界高度确定方法。对极限状态下加筋土坡临界高度的目标函数,采用序列二次规划非线性优化算法,得到上限解。最后,通过工程算例分析,并与已有离心模型试验结果和理论研究方法计算结果进行对比,结果表明本文计算方法考虑间断面滑动层筋材与土体变形协调及速度变化连续性是合理的,得到的上限解更优;非线性参数对加筋土坡稳定性有着重要影响,临界高度随非线性参数m的增大而减小。 相似文献
11.
Three-dimensional slope reliability and risk assessment using auxiliary random finite element method
This paper aims to propose an auxiliary random finite element method (ARFEM) for efficient three-dimensional (3-D) slope reliability analysis and risk assessment considering spatial variability of soil properties. The ARFEM mainly consists of two steps: (1) preliminary analysis using a relatively coarse finite-element model and Subset Simulation, and (2) target analysis using a detailed finite-element model and response conditioning method. The 3-D spatial variability of soil properties is explicitly modeled using the expansion optimal linear estimation approach. A 3-D soil slope example is presented to demonstrate the validity of ARFEM. Finally, a sensitivity study is carried out to explore the effect of horizontal spatial variability. The results indicate that the proposed ARFEM not only provides reasonably accurate estimates of slope failure probability and risk, but also significantly reduces the computational effort at small probability levels. 3-D slope probabilistic analysis (including both 3-D slope stability analysis and 3-D spatial variability modeling) can reflect slope failure mechanism more realistically in terms of the shape, location and length of slip surface. Horizontal spatial variability can significantly influence the failure mode, reliability and risk of 3-D slopes, especially for long slopes with relatively strong horizontal spatial variability. These effects can be properly incorporated into 3-D slope reliability analysis and risk assessment using ARFEM. 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.
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
14.
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
15.
针对土坡稳定体系可靠度分析问题,提出了一种分析方法框架,包括采用缩减方差抽样技术生成随机变量样本值、采用全局优化算法搜索边坡最小安全系数、采用Monte-Carlo法计算边坡体系可靠度3个主要部分。在此框架下,建立了一种较为简便实用的高土石坝坝坡稳定体系可靠度分析方法。该方法采用拉丁超立方抽样技术生成随机变量的样本值,再用商业软件STAB搜索相应的坝坡最小安全系数,最后用可靠指标法或Monte-Carlo法计算坝坡体系可靠度。工程算例表明,筑坝材料强度参数的随机不确定性对坝坡临界滑弧的位置影响较大,坝坡稳定体系可靠度小于单一滑动面的坝坡稳定最小可靠度,提出的方法可用于实际工程坝坡稳定体系可靠度分析。 相似文献
16.
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
17.
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