Discretization of 2D random fields: A genetic algorithm approach

The random fields are widely used in the literature to include the random spatial variability of material properties, geometrical dimensions and loads into the mathematical models describing the behaviour of the engineering structures. In practice, a discretization procedure is necessary to reduce a continuous random field to a finite set of random variables. It is clear that the accuracy of the discretization is a key point for any subsequent probabilistic investigation of the structural performance. Indeed it is meaningful to formulate a discretization error estimator to quantify the accuracy of the discretization and to require that the approximated random field fulfils a prescribed target accuracy. The discretization of a random field can be formulated as an optimization problem, where the optimization variable is the finite number of random variables involved in the representation of the random field and the objective function depends on the discretization error estimator and on the target accuracy. The advantage of the formulation of the optimization problem depends on the availability of a simple, accurate and versatile numerical approach. A genetic algorithm is proposed in this paper to achieve an optimal discretization of two-dimensional (2D) homogeneous random fields represented by the Karhunen–Loeve series expansion. The numerical procedure is applied to the discretization of 2D random fields describing the random spatial fluctuations of the concrete properties in a bridge deck.     

Expanded reliability-based design of piles in spatially variable soil using efficient Monte Carlo simulations

Inherent spatial variability is considered as a major source of uncertainties in soil properties, and it affects significantly the performance of geotechnical structures. However, research that considers, directly and explicitly, the inherent spatial variability in reliability-based design (RBD) of geotechnical structures is limited. This paper develops a RBD approach that integrates a Monte Carlo Simulation (MCS)-based RBD approach, namely the expanded RBD approach, with random field theory to model, both directly and explicitly, the inherent spatial variability of soil properties in RBD of drilled shafts. The proposed approach is implemented in a commonly-available spreadsheet environment to effectively remove the hurdle of reliability computational algorithms and to provide a user-friendly graphical user interface to practicing engineers. To improve the efficiency and resolution of MCS at small probability levels, the expanded RBD approach is enhanced with an advanced MCS method called “Subset Simulation”. Equations are derived for the integration of the expanded RBD approach and Subset Simulation. The proposed approach is illustrated through a drilled shaft design example, and is applied to explore the effects of inherent spatial variability (including the scale of fluctuation and correlation structure) and to evaluate systematically the equivalent variance technique that is commonly used to indirectly model inherent spatial variability in current RBD approaches. It is found that inherent spatial variability significantly affects the RBD of drilled shafts, and its effects are considered in RBD using the proposed approach in a direct and explicit manner. In addition, the results show that the indirect modeling of inherent spatial variability using the equivalent variance technique with the simplified form of variance reduction function in RBD might lead to relatively conservative designs in design practice.     

Soil spatial variability impact on the behavior of a reinforced earth wall

This article presents the soil spatial variability effect on the performance of a reinforced earth wall. The serviceability limit state is considered in the analysis. Both cases of isotropic and anisotropic non-normal random fields are implemented for the soil properties. The Karhunen-Loève expansion method is used for the discretization of the random field. Numerical finite difference models are considered as deterministic models. The Monte Carlo simulation technique is used to obtain the deformation response variability of the reinforced soil retaining wall. The influences of the spatial variability response of the geotechnical system in terms of horizontal facing displacement is presented and discussed. The results obtained show that the spatial variability has an important influence on the facing horizontal displacement as well as on the failure probability.     

Estimation of the probability distribution of the random bearing capacity of cohesionless soil using the random finite element method

Soil mathematical modelling is a complex task when considering random soil parameters. To consider soil spatial variability, one can assume that soil properties are random variables or random fields. This study considered the estimation of the probability distribution of the bearing capacity for shallow footings in cohesionless soil using the random finite element method. In the deterministic case, the FEM code was calibrated to receive capacity evaluations similar to the results obtained using Hill's mechanism. Final capacity simulations were conducted under the assumption that the friction angle of the investigated subsoil constitutes a random field characterised by a bounded distribution. The random field was obtained by applying a hyperbolic tangent transformation to a Gaussian random field with an ellipsoidal (anisotropic) correlation function. The final outcome of the numerical analysis was an evaluation of the probability distribution that fits the bearing capacity for various foundation widths and depths. In all the cases considered, the empirical probability distribution of the bearing capacity closely resembled the Weibull distribution. Moreover, the estimated distribution of the bearing capacity was used as the basis for a foundation safety assessment.     

考虑参数空间变异性的无限长边坡可靠度分析

目前有关土体参数空间变异性对边坡稳定性影响的研究没有考虑抗剪强度参数随深度变化的影响。为此,提出了考虑土体抗剪强度参数均值随深度变化的无限长边坡稳定性概率分析方法。采用Karhunen-Loeve展开建立了表征土体空间变异性的随机场模型。探讨了考虑土体抗剪强度参数空间变异性时边坡失效概率和最危险滑动面的变化规律。最后,以无限长边坡稳定性概率分析问题为例验证了所提方法的有效性。结果表明：土体抗剪强度参数的空间变异性对无限长边坡失效概率有明显的影响,边坡失效概率随土体抗剪强度参数相关距离的增加而减小。对于不排水黏性边坡来说,边坡不排水抗剪强度随深度变化越明显,边坡失效概率越小。边坡最危险滑动面大部分都位于边坡底部。对于摩擦/黏性边坡来说,随着边坡抗剪强度随深度变化的增强,边坡失效概率有先减后增的趋势。抗剪强度参数随深度变化不同趋势对最危险滑动面分布规律有明显的影响。不考虑抗剪强度参数均值随深度变化将会导致最危险滑动面最可能出现在边坡底部。     

考虑参数空间变异性的边坡可靠度分析非侵入式 随机有限元法

提出了考虑土体参数空间变异性的边坡可靠度分析的非侵入式随机有限元法。采用Karhunen-Loeve级数展开方法表征土体抗剪强度参数空间变异性,其中通过wavelet-Galerkin技术求解Fredholm积分方程得到相关函数的特征解。基于有限元滑面应力法计算边坡安全系数,采用随机多项式展开将隐式函数表达的安全系数替换为显式函数表达的安全系数,并编写了计算程序NISFEM。研究了所提方法在考虑土体参数空间变异性的边坡稳定可靠度分析中的应用。结果表明：提出的非侵入式随机有限元法极大地提高了考虑土体参数空间变异性的边坡可靠度分析的计算效率,为解决复杂边坡稳定可靠度问题提供了一条有效的途径。考虑抗剪强度参数空间变异性的边坡可靠度分析存在临界变异系数,其随边坡安全系数的增加而增大。当抗剪强度参数的变异系数小于临界变异系数时,忽略土体参数空间变异性将会高估边坡失效概率。当边坡安全系数小于1时,边坡失效概率并不总是随着抗剪强度变异系数的增加而增大。此外,土体黏聚力和内摩擦角随机场间相关性对边坡失效概率具有十分明显的影响。     

Reliability of pressurised pipelines subjected to longitudinal ground movement

A methodology was developed to calculate the probabilities of tensile rupture and compressive local buckling for pressurised pipelines that are buried in unstable slopes and subjected to longitudinal loading imposed by the slopes. The limit state functions were established by comparing the tensile and compressive strain demand with the corresponding strain capacities. The strain demand is calculated using an analytical model reported in the literature, which is capable of accounting for the elastic–plastic behaviour of the pipeline and the elastic–perfectly plastic behaviour of the pipe–soil friction. The strain capacities are characterised based on the information available in the literature. The spatial variability of the soil and pipe properties is incorporated in a simplified manner. A numerical example was used to illustrate the proposed methodology and impact of the spatiality variability of the soil and pipe properties on the failure probability. The analysis results suggest that the spatial variability of pipe wall thickness and yield strength tends to increase the failure probability, whereas the spatial variability of the soil ultimate resistance tends to decrease the failure probability.     

各向异性随机场下的边坡模糊随机可靠度分析

土性参数具有很大的空间变异性,且在水平方向和垂直方向上差异显著。基于随机变量模型的传统边坡模糊随机可靠度分析方法并未对此进行考虑。提出一种能合理考虑土性参数空间变异性的边坡模糊随机可靠度分析方法。首先,视黏聚力和内摩擦角的均值为正态模糊数,对其取不同的λ截集水平并在各截集水平上进行参数组合。其次,利用各向异性随机场模拟土性参数的空间变异性,将有限元法和Monte–Carlo模拟相结合,计算各参数组合对应的可靠度指标。再通过数学方法得到边坡在各截集水平上的可靠度指标。最后,运用加权平均法计算边坡的模糊随机可靠度指标。算例分析表明:与水平方向的空间变异性相比,垂直方向的空间变异性对边坡模糊随机可靠度的影响更为显著;不考虑土性参数的空间变异性在一般情况下会低估边坡的模糊随机可靠度指标,但在抗剪强度参数变异性较大时,反而可能会高估边坡的模糊随机可靠度指标;此外,黏聚力与内摩擦角之间的相关性对边坡失效概率的影响趋势基本不受土性参数空间变异性的干扰。     

Probabilistic service life of reinforced concrete structures with randomly distributed corrosion-induced cracks

This paper proposed a probabilistic service life prediction method for reinforced concrete (RC) structures with randomly distributed chloride corrosion-induced cracking. In the proposed method, spatial randomness of environmental, geometric and physical factors was considered that influence corrosion process and crack propagation of RC structures at the material level. Karhunen–Loéve (KL) expansion method was utilised for modelling the spatial random fields. Four limit state functions were proposed based on four deterioration events (corrosion initiation, surface crack initiation, modulus degradation and exceedance of repair limit). Then, time-dependent reliability analyses of an RC bridge slab were conducted using the proposed method. Finally, sensitivity analysis of the statistical parameters including mean, variance, correlation length as well as the truncation number for KL expansion method were conducted to determine the effects of those parameters to the service life.     

High-order limit state functions in the response surface method for structural reliability analysis

The stochastic response surface method (SRSM) is a technique for the reliability analysis of complex systems with low failure probabilities, for which Monte Carlo simulation (MCS) is too computationally intensive and for which approximate methods are inaccurate. Typically, the SRSM approximates a limit state function with a multi-dimensional quadratic polynomial by fitting the polynomial to a number of sampling points from the limit state function. This method can give biased approximations of the failure probability for cases in which the quadratic response surface can not conform to the true limit state function’s nonlinearities. In contrast to recently proposed algorithms which focus on the positions of sample points to improve the accuracy of the quadratic SRSM, this paper describes the use of higher order polynomials in order to approximate the true limit state more accurately. The use of higher order polynomials has received relatively little attention to date because of problems associated with ill-conditioned systems of equations and an approximated limit state which is very inaccurate outside the domain of the sample points. To address these problems, an algorithm using orthogonal polynomials is proposed to determine the necessary polynomial orders. Four numerical examples compare the proposed algorithm with the conventional quadratic polynomial SRSM and a detailed MCS.     

Random checkerboard based homogenization for estimating effective thermal conductivity of fully saturated soils

This paper proposes homogenization scheme for estimating the effective thermal conductivity of fully saturated soils. This approach is based on the random checkerboard-like microstructure. Two modeling scales and two modeling approaches are distinguished and used, i.e. microscale and mesoscale and 1-step and 2-step homogenizations, respectively. The 2-step homogenization involves sequential averaging procedure, i.e. first, at microscale, a mineralogical composition of soil skeleton is considered and averaging process results in estimation of the skeleton effective thermal conductivity, and then, at mesoscale, a random spatial packing of solid skeleton and pores via random checkerboard microstructure is modeled and leads to evaluation of the soil overall thermal conductivity. The 1-step homogenization starts directly at the mesoscale and homogenization procedure yields evaluation of the overall soil thermal conductivity. At the mesoscale, the distinct nature of soil skeleton, as composed of soil separates, is considered and random variability of soil is modeled via enriched random checkerboard-like structure. Both approaches, i.e. 1-step and 2-step homogenizations, interrelate mineralogical composition with the soil texture characterized by the volume fractions of soil separates, i.e. sand, silt and clay. The probability density functions (PDFs) of thermal conductivity are assumed for each of the separates. The soil texture PDF of thermal conductivity is derived taking into consideration the aforementioned functions. Whenever the random checkerboard-like structure is used in averaging process, the Monte Carlo procedure is applied for estimation of homogenized thermal conductivity. Finally, the proposed methodology is tested against the laboratory data from our measurements as well as those available from literature.     

An analytical method for quantifying the correlation among slope failure modes in spatially variable soils

An efficient analytical method for quantifying the correlation between performance functions of different slope failure modes in spatially variable soils is proposed, and its performance in slope system reliability analysis is investigated. First, a new correlation coefficient (NCC) is proposed to evaluate the correlation among slope failure modes considering spatial variability. For comparison and verification, the simulation-based correlation coefficient (SCC) is also presented. Second, appying these two types of correlation coefficients, the effects of soil spatial variability on the representative slip surfaces (RSSs) and the system probability of slope failure are investigated using different system reliability methods, including a probabilistic network evaluation technique, a risk aggregation approach, and a bimodal bounds method. A single-layered cohesive slope is investigated to illustrate the validity of the proposed NCC. The results indicate that the proposed NCC can efficiently and accurately quantify the correlation among slope failure modes considering soil spatial variability. The number of RSSs indicated by the NCC is in good agreement with the number obtained using the SCC. The system failure probabilities of slope stability obtained with the SCC and the NCC using a risk aggregation approach are generally comparable. Also, the system reliability bounds of slope stability obtained using the NCC are relatively close together and comparable to those obtained using the SCC. Thus, the NCC shows good performance when evaluating the correlation among slope failure modes, and was effectively applied to analyze a single-layered cohesive slope considering soil spatial variability.     

双层地基一维固结平均固结度敏感性分析

针对竖向固结系数可能服从Gamma分布、对数正态分布或Beta分布,利用两点概率估计理论进行双层地基一维固结平均固结度的敏感性分析,研究了竖向固结系数变异性和分布概型对平均固结度的影响,为进一步开展多层地基固结的可靠性分析打下了基础。     

考虑土体空间变异性的边坡最危险滑动面随机分析方法

现有边坡可靠度研究均未考虑土体空间变异性对边坡最危险滑面的影响。为此，提出了考虑土体空间变异性的边坡最危险滑动面随机分析方法。首先，采用谱表现法建立了表征土体空间变异性的随机场模型。在此基础上，提出了基于SIGMA/W和SLOPE/W的自动定位搜索最危险滑动面方法。其次，采用非侵入式随机分析方法研究了抗剪强度参数空间变异性对边坡最危险滑动面空间分布的影响。最后，采用算例验证了所提方法的有效性。结果表明：提出的边坡最危险滑动面随机分析方法能够有效地确定边坡最危险滑动面空间分布特征。土体抗剪强度参数的空间变异性对边坡最危险滑动面的空间分布特征有重要的影响，它直接决定了边坡最危险滑动面的位置和滑体规模。土体抗剪强度参数波动范围越大，最危险滑动面的空间分布范围越大。随着土体抗剪强度参数水平向和竖直向波动范围比值的增大，边坡上部发生局部滑动的可能性增大。抗剪强度参数的变异系数越大，最危险滑动面的空间分布范围越大，边坡发生小规模局部滑动的可能性越大。     

考虑参数空间变异性的隧道结构变形分析简化方法

土体参数具有空间变异性是被广泛接受的,而这种变异性对岩土中结构性能有着重要的影响.随机场理论是一种常用的用来模拟土体参数空间变异性的方法.基于随机场理论,以土体弹性模量的空间变异性为切入点,采用蒙特卡罗方法和有限差分模拟计算相结合的方法,开展隧道水平收敛的随机分析.在大量蒙特卡罗计算基础上,提出了3种简单易用的简化考虑...     

考虑不排水抗剪强度空间变异性的条形基础极限承载力随机分析

目前有关抗剪强度参数随深度变化对地基稳定性影响的研究还不深入。为此,提出了考虑土体不排水抗剪强度均值和标准差随深度变化的地基稳定性随机分析方法。建立了表征不排水抗剪强度空间变异性的不平稳随机场模型,采用Karhunen-Loeve（KL）展开离散随机场。探讨了土体不排水抗剪强度参数空间变异性对地基极限承载力的影响规律,并比较了不排水抗剪强度参数平稳和不平稳随机场模型对地基稳定的影响。以不排水黏性地基稳定随机分析问题为例验证了所提方法的有效性。结果表明：考虑不排水抗剪强度参数空间变异性时,地基极限承载力均值和标准差随相关距离的增大而增大,地基极限承载力对竖直向相关距离更为敏感。地基极限承载力均值随不排水抗剪强度变异系数的增加而减小,标准差随变异系数的增加而增加。不排水抗剪强度变异性对地基失效概率有明显的影响,安全系数较大时,不排水抗剪强度相关距离越小,地基失效概率越小。与不排水抗剪强度参数的不平稳随机场相比,不排水抗剪强度的平稳随机场模型会高估地基极限承载力的变异性,在相同的安全度水平下,当地基的安全系数较低时,平稳随机场模型会导致对地基失效概率的低估;当地基安全系数较高时,平稳随机场模型会导致对地基失效概率的高估。     

Incorporating installation effects into the probability analysis of controlled modulus columns

This technical report presents the probabilistic analysis which integrates the Monte Carlo simulation (MCS) with random field theory to model the load–displacement behavior of Controlled Modulus Columns (CMCs) in overconsolidated Poznań clay. Presented study focuses on the practical aspects of statistical analysis of geotechnical data, numerical model development, and results evaluation. Variability and spatial distribution of geotechnical parameters are based directly on field and lab testing. The inherent variability of soil parameters obtained from geotechnical investigation at the site is similar to the values reported in worldwide datasets for clays. The extensive discussion about incorporation of installation effects into numerical modelling is made. It was found that proper incorporation of installation effects is governed by correct estimation of initial stress level and interface shear strength parameters. The Anisotropic Undrained Shear Strength (AUS) model which captures nonlinear behavior and anisotropy of soil (Krabbenhøft et al., 2019) is a good choice to model overconsolidated clay in intact and interface zones. The application of total stress approach, the AUS model, installation effects, and natural (inherent) variability of soil and interface parameters is sufficient to explain differences in CMC load – displacement behavior observed in the field.     

非平稳随机场下饱和渗透系数空间变异性的无限长边坡稳定概率分析

土体饱和渗透系数表现为天然的空间强变异性,其均值通常沿土层深度方向呈递减趋势,但目前鲜有文献考虑这一特性。为此,以FLAC为平台,利用fish语言基于局部平均法建立了表征土体饱和渗透系数均值随深度递减的一维非平稳随机场模型,采用FLAC两相流模块模拟雨水的入渗过程,以蒙特卡罗法为框架,结合考虑正孔隙水压力的广义有效应力无限边坡稳定模型,探讨了饱和渗透系数空间变异性在不同降雨时刻下对边坡最危险滑裂面分布规律以及相应的边坡破坏概率的影响。结果表明：忽略饱和渗透系数的空间变异性的确定分析方法将不能真实反映边坡的安全性;随着降雨持时的增加,边坡最危险滑裂面发生在坡底基岩处的概率逐渐降低;随着饱和渗透系数竖向相关距离的增加,边坡最危险滑裂面发生在坡底的概率逐渐增加,而相应的边坡破坏概率却随竖向相关距离的增加而逐渐降低。     

边坡可靠度分析的随机响应面法及程序实现

提出分析相关非正态变量可靠度计算问题的随机响应面法,采用Nataf变换成功地解决输入变量相关时随机响应面法的配点问题及可靠度计算问题。推导4～6阶Hermite随机多项式展开的解析表达式,并编写基于C#语言的随机响应面法计算程序。以岩质边坡平面滑动破坏模式为例证明随机响应面法在边坡可靠度分析中的有效性。研究结果表明,基于Nataf变换的随机响应面法能够有效分析含有相关非正态变量的边坡可靠度问题。随机响应面法的计算精度优于传统的FORM方法,其计算效率高于传统的蒙特卡罗模拟方法,其收敛性在数学意义上是有保证的。随机多项式展开的阶数几乎对边坡安全系数均值的估计没有影响,但是在边坡失效概率的计算中要选择适当的随机多项式展开的阶数。在基于随机响应面法的可靠度分析框架内,边坡安全系数计算和可靠度分析2个过程分开独立进行,同时计算安全系数和失效概率能够更加系统地进行边坡稳定性分析。研究成果为拓展随机响应面法在边坡可靠度分析中的应用奠定了一定的基础。     

钢管混凝土徐变效应随机性灵敏度分析

针对钢管混凝土徐变的随机性,利用支持向量机回归拟合钢管混凝土徐变效应的显式函数计算随机变量的灵敏度系数,并结合蒙特卡洛法进行随机性分析;采用自适应混合粒子群法优化支持向量机相关参数的取值以提高计算效率;对2个钢管混凝土徐变模型试验构件进行徐变随机性分析,并将计算结果与蒙特卡洛法计算结果进行对比验证了该方法的可行性;同时分析了钢管混凝土徐变效应各影响因素的灵敏度。结果表明:基于支持向量机与蒙特卡洛法对钢管混凝土轴压构件徐变随机性的分析结果与蒙特卡洛法分析结果相比相对误差较小;钢管混凝土徐变效应呈现随机性,概率密度曲线近似于正态分布。