渗透系数对砂土液化震陷影响的数值研究

饱和砂土在地震荷载的作用下往往会产生液化变形,包括竖向震陷和侧向扩展。砂土由于液化的作用,其渗透系数会发生改变,而目前描述砂土液化变形的本构模型均采用常量渗透系数,这是造成自由场地的震陷数值模拟结果低于试验观测值的重要原因之一。利用开源有限元平台Open Sees对饱和砂土的自由场地震陷进行模拟分析,对比离心机模型试验,分析了渗透系数对饱和砂土液化震陷的影响。为进一步提高数值模拟的准确性,采用了适合于动力分析及液化模拟的边界面模型。与固定渗透系数模型相比,最终提出的变渗透系数模型允许液化状态的渗透系数升高为初始值的数倍,该模型模拟结果较好,可以作为从渗透模型角度提高数值模拟精度的近似考虑。通过一系列的模拟和分析发现,采用合理的变渗透系数模型,可在一定程度上提高砂土自由场地地震液化震陷的数值模拟精度。     

纤维加筋砂土抗液化试验与数值模拟

砂土液化是导致重大地震灾害的主要原因之一。本研究探讨了天然纤维加筋砂土在循环荷载作用下的抗液化性能。在不排水条件下,对具有不同纤维含量的加筋砂土试样进行了一系列循环三轴试验,研究了饱和砂土的液化特性以及循环剪应变幅值、纤维含量对饱和砂土抗液化性能的影响。此外,通过模拟已完成的循环三轴试验,建立了二维有限元数值模型,并对具有不同纤维含量的加筋砂土进行了参数标定。研究结果表明:(1)增加循环剪应变幅值将促进超孔压累积,使得滞回曲线斜率和平均有效应力减小速度加快;(2)纤维的存在能够减缓超孔压的累积,随着纤维含量增加,加筋砂土抗液化能力得到明显提高;(3)标定后的本构模型参数能可靠地用于模拟纤维加筋砂土的液化响应。研究结果为饱和砂土抗液化问题与纤维加筋砂土的数值模拟提供了有价值的参考。     

循环荷载下砂土液化特性颗粒流数值模拟

利用PFC2D常体积循环双轴试验条件,对砂土在不排水循环荷载作用下的液化特性进行了颗粒流数值模拟,数值模拟按等应力幅加荷方式进行。颗粒流数值模拟的优点在于得到试样液化宏观力学表现的同时,通过不同循环加荷时刻试样内细观组构参量（包括配位数、接触法向分布、粒间法向接触力、粒间切向接触力）的演化规律,分析砂土液化过程中细观组构变化与宏观力学响应之间的内在联系,从而可进一步探讨砂土液化的细观力学机制。数值模拟研究结果表明,砂土液化现象在宏观力学表现上反映为超静孔隙水压力的累积上升和平均有效主应力的不断减小,在细观组构上对应于配位数的累积损失和粒间接触力的不断减小。砂土液化细观机制分析表明,试样配位数的减少与循环加荷过程中组构各向异性滞后于应力各向异性有关。     

饱和砂土液化后大变形机制的离散元细观分析

采用颗粒流软件模拟了饱和砂土在不排水条件下的循环剪切试验,研究了不同因素对液化的影响,并进一步分析了饱和砂土液化后宏观变形的基本规律。在此基础上,从孔隙分布角度解释了砂土液化后的大变形的细观物理机制。通过自编程序对颗粒排列和孔隙分布的演化过程进行定量描述,给出孔隙率标准差作为液化后体积收缩势的度量,并研究了孔隙率标准差与液化后大变形的关系。离散元细观数值模拟再现了室内试验中的宏观现象,证实了室内试验中饱和砂土液化后的有限剪切大变形是客观真实的材料响应。土体体积收缩势的累积所导致的孔隙均匀化以及土颗粒间自由空隙增大正是饱和砂土液化后循环剪应变逐渐增大的细观机制。孔隙率标准差作为孔隙均匀化的量化指标,与循环剪应变各周次幅值有良好的相关性。     

饱和砂土液化判别与放大效应数值模拟研究

为研究地震作用下饱和砂土液化判别及地震放大效应的影响因素,采用边界面塑性模型框架内开发的砂土本构模型,基于开源有限元平台OpenSees建立了一维剪切梁土柱模型。以循环应力比CSR和循环抗力比CRR为控制指标,对比了不同液化判别方法的差异,分析了地震荷载类型和砂土相对密度对液化判别和放大效应的影响。研究表明：与数值模拟结果相比,Seed简化法计算的CSR更大,判断饱和砂土场地发生液化的可能性更高;冲击型地震波较振动型地震波更容易使饱和砂土场地发生液化,砂土相对密度越小场地越容易发生液化;放大系数随埋深的减小而增大,振动型地震波引起的放大效应整体大于冲击型,埋深较大时放大系数随砂土相对密度的增大而减小。     

饱和层状砂土液化特性的动三轴试验研究

利用GDS动三轴试验系统采用等幅循环应变加载方式对含有不同厚度粉土的饱和层状砂土进行了液化强度试验。分析了均匀砂和含有不同粉粒层厚度的层状砂土在循环荷载作用下的变形和力学特性。试验分析表明：由于含粉粒夹层的层状土特殊的土体结构,其孔隙水压力发展规律与一般的无黏性砂土不同;饱和层状砂土的抗液化强度并不是随着粉粒层厚度的增加而单调增加的,而是存在一个临界点;液化临界剪应变的大小与液化判别标准和循环次数有很大关系。试验结果表明,粉粒夹层对层状砂土的液化特性有很大的影响,且更能模拟自然环境条件下的层状砂土地基液化特性。     

液化砂土流动效应的振动台试验研究

采用振动台激励使饱和砂土发生液化,并侧向拖拽埋入砂土中的铝管,模拟液化土体与管体相对运动以分析液化砂土流动的力学效应。引入流体力学理论与方法,推导出以拉力反算表观黏度的表达式以及液化土体作为流体对管壁作用的黏滞剪切力。分析和比较了振时拖动、振后拖动下土体的流体性质及其流动效应的率相关性和孔压相关性,探讨了砂土密实度对土体流动效应的影响。结果表明,土体初始密实度与液化后土体的表观黏度正相关;液化土体的表观黏度以及因流动产生的黏滞剪切力与孔压反相关;液化砂土流动产生的黏滞剪切力具有强烈的率相关性。针对可液化场地中的结构抗震分析,应考虑土-结构率相关相互作用。     

饱和砂土液化后大变形机理的离散元细观分析

采用颗粒流软件模拟了饱和砂土在不排水条件下的循环剪切试验,研究了不同因素对液化的影响,并进一步分析了饱和砂土液化后宏观变形的基本规律。在此基础上,从孔隙分布角度解释了砂土液化后的大变形的细观物理机制。通过自编程序对颗粒排列和孔隙分布的演化过程进行定量描述,给出孔隙率标准差作为液化后体积收缩势的度量,并研究了孔隙率标准差与液化后大变形的关系。结果表明,试样初始状态并不会影响液化后的极限状态,仅对初始液化所需循环次数有影响。数值模拟可再现室内试验中的宏观现象,证实了室内试验中饱和砂土液化后的有限剪切大变形是客观真实的材料响应。土体体积收缩势的累积所导致的孔隙均匀化以及土颗粒间自由空隙增大正是饱和砂土液化后循环剪应变逐渐增大的细观机理。孔隙率标准差作为孔隙均匀化的量化指标,与循环剪应变各周次幅值有良好相关性。     

饱和砂土液化后大变形机理的离散元细观分析

采用颗粒流软件模拟了饱和砂土在不排水条件下的循环剪切试验,研究了不同因素对液化的影响,并进一步分析了饱和砂土液化后宏观变形的基本规律。在此基础上,从孔隙分布角度解释了砂土液化后的大变形的细观物理机制。通过自编程序对颗粒排列和孔隙分布的演化过程进行定量描述,给出孔隙率标准差作为液化后体积收缩势的度量,并研究了孔隙率标准差与液化后大变形的关系。结果表明,试样初始状态并不会影响液化后的极限状态,仅对初始液化所需循环次数有影响。数值模拟可再现室内试验中的宏观现象,证实了室内试验中饱和砂土液化后的有限剪切大变形是客观真实的材料响应。土体体积收缩势的累积所导致的孔隙均匀化以及土颗粒间自由空隙增大正是饱和砂土液化后循环剪应变逐渐增大的细观机理。孔隙率标准差作为孔隙均匀化的量化指标,与循环剪应变各周次幅值有良好相关性。     

渭河盆地东南部砂土液化模糊综合评价及洪水影响研究

依据砂土液化影响因素和大量实际资料对渭河盆地东南部5县进行了砂土液化模糊综合评价, 给出了砂土液化分区, 并讨论了洪水泛滥对该区砂土液化的影响。     

Two Artifacts of Probability Field Simulation

Probability field simulation is being used increasingly to simulate geostatistical realizations. The method can be faster than conventional simulation algorithms and it is well suited to integrate prior soft information in the form of local probability distributions. The theoretical basis of probability field simulation has been established when there are no conditioning data; however, no such basis has been established in presence of conditioning data. Realizations generated by probability field simulation show two severe artifacts near conditioning data. We document these artifacts and show theoretically why they exist. The two artifacts that have been investigated are (1) local conditioning data appear as local minima or maxima of the simulated values, and (2) the variogram model in range of conditioning data is not honored; the simulated values have significantly greater continuity than they are supposed to. These two artifacts are predicted by theory. An example flow simulation study is presented to illustrate that they affect more than the visual appearance of the simulated realizations. Notwithstanding the flexibility of the probability field simulation method, these two artifacts suggest that it be used with caution in presence of conditioning data. Future research may overcome these limitations.     

利用全概率方法分析岩土力学数值模拟结果的变异性

提出了一种用于分析在多参数随机情况下岩土力学数值模拟结果变异性的全概率方法。以特定数值模拟结果为随机隐式响应量,根据多维高效数值求积理论,计算出响应量特定函数的均值,利用快速傅里叶变换数值概率分析方法及笔者开发的最大熵分布,求解器软件一次性求解出响应量的全概率分布,用于定量评估数值模拟结果的变异性。算例表明该法具有精度和计算效率较高的优点。     

Probability field for the post-processing of stochastic simulations

A combination of factorial kriging and probability field simulation is proposed to correct realizations resulting from any simulation algorithm for either too high nugget effect (noise) or poor histogram reproduction. First, a factorial kriging is done to filter out the noise from the noisy realization. Second, the uniform scores of the filtered realization are used as probability field to sample the local probability distributions conditional to the same dataset used to generate the original realization. This second step allows to restore the data variance. The result is a corrected realization which reproduces better target variogram and histogram models, yet honoring the conditioning data.     

蒙特卡洛模拟方法在斜坡稳定性评价中的应用

介绍了蒙特卡洛随机模拟的基本原理及在斜坡稳定性评价中的应用，给出了斜坡的破坏概率，模拟的次数与所给误差之间的关系，以岩村滑坡为例进行了计算，结果与实际相吻合。     

基于破坏概率的滑坡稳定性分析

综述了概率分析法的基本原理和实施方法,并运用Monte carlo模拟对滑坡的破坏概率进行了模拟分析。在探讨具体工程实例的基础上,提出了计算机处理滑坡稳定性的程序设计方法。实践表明,滑坡的破坏概率分析是滑坡稳定性评价的有效手段之一。      

自然电场的概率成像方法评价

对概率成像方法的原理作了详细的阐述,并对一些简单的有规则的模型进行了成像模拟;分析了扫描函数的选取、分辨能力、局限等对概率成像法的分辨能力的影响.     

High-Order Spatial Simulation Using Legendre-Like Orthogonal Splines

High-order sequential simulation techniques for complex non-Gaussian spatially distributed variables have been developed over the last few years. The high-order simulation approach does not require any transformation of initial data and makes no assumptions about any probability distribution function, while it introduces complex spatial relations to the simulated realizations via high-order spatial statistics. This paper presents a new extension where a conditional probability density function (cpdf) is approximated using Legendre-like orthogonal splines. The coefficients of spline approximation are estimated using high-order spatial statistics inferred from the available sample data, additionally complemented by a training image. The advantages of using orthogonal splines with respect to the previously used Legendre polynomials include their ability to better approximate a multidimensional probability density function, reproduce the high-order spatial statistics, and provide a generalization of high-order simulations using Legendre polynomials. The performance of the new method is first tested with a completely known image and compared to both the high-order simulation approach using Legendre polynomials and the conventional sequential Gaussian simulation method. Then, an application in a gold deposit demonstrates the advantages of the proposed method in terms of the reproduction of histograms, variograms, and high-order spatial statistics, including connectivity measures. The C++ course code of the high-order simulation implementation presented herein, along with an example demonstrating its utilization, are provided online as supplementary material.     

Failure probability assessment and parameter sensitivity analysis of a contaminant’s transit time through a compacted clay liner

This paper presents failure probability assessment and parameter sensitivity analysis of a contaminant’s transit time through a compacted clay liner. Monte Carlo simulation (MCS) was used to assess failure probability, and the failure samples generated in the MCS were used to investigate the sensitivity of various uncertain parameters to the failure probability. To facilitate the MCS, a database on various transport parameters was developed by collecting and analyzing measurement data reported in literature. Failure probability assessment and parameter sensitivity analysis showed that the uncertainties in adsorption parameters, longitudinal dispersivity, and hydraulic conductivity have the most significant effects on failure probability.     

Conditional random field reliability analysis of a cohesion-frictional slope

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.     

Markov Chain Random Fields for Estimation of?Categorical Variables

Multi-dimensional Markov chain conditional simulation (or interpolation) models have potential for predicting and simulating categorical variables more accurately from sample data because they can incorporate interclass relationships. This paper introduces a Markov chain random field (MCRF) theory for building one to multi-dimensional Markov chain models for conditional simulation (or interpolation). A MCRF is defined as a single spatial Markov chain that moves (or jumps) in a space, with its conditional probability distribution at each location entirely depending on its nearest known neighbors in different directions. A general solution for conditional probability distribution of a random variable in a MCRF is derived explicitly based on the Bayes’ theorem and conditional independence assumption. One to multi-dimensional Markov chain models for prediction and conditional simulation of categorical variables can be drawn from the general solution and MCRF-based multi-dimensional Markov chain models are nonlinear.