基于多源信息融合的路堤沉降预测方法

准确预测路堤沉降对于规避风险和减小成本至关重要。传统的仅基于场地勘察数据的路堤沉降预测方法的预测值常偏离监测值。提出了基于贝叶斯理论的多源信息融合方法进行路堤沉降预测,采用有限元法模拟多层土体的固结沉降,并结合高效的马尔科夫链蒙特卡洛模拟法更新土体参数得到高维后验分布。以新南威尔士州的Ballina地区试验路堤数据为例说明了所提方法的有效性。结果表明,基于贝叶斯理论的多源信息融合方法可以有效融合勘察和监测数据,通过多源数据融合能够较准确地预测路堤沉降。对于Ballina路堤,总体上随着监测数据量的增加,路堤沉降预测预测精度逐渐提高。使用0～116 d监测数据可以准确地预测地表沉降;基于0～496 d监测数据可同时准确预测所有监测点的沉降。对于Ballina路堤,先验信息对沉降预测具有一定影响,但观测误差对预测准度影响微弱。     

Bayesian updating of KJHH model for prediction of maximum ground settlement in braced excavations using centrifuge data

In this paper, a Bayesian approach for updating a semi-empirical model for predicting excavation-induced maximum ground settlement using centrifuge test data is presented. The Bayesian approach involves three steps: (1) prior estimate of the maximum ground settlement and model bias factor, (2) establishment of the likelihood function and posterior distribution of the model bias factor using the settlement measurement in the centrifuge test, and (3) development of posterior distribution of the predicted maximum settlement. This Bayesian approach is demonstrated with a case study of a well-documented braced excavation, and the results show that the accuracy of the maximum settlement prediction can be improved and the model uncertainty can be reduced with Bayesian updating.     

Modeling municipal solid waste landfill settlement

A good prediction of solid waste landfill settlement is important for landfill design and rehabilitation. A one-dimensional model which accounts for mechanical settlement and biodegradation processes is developed to simulate the settlement behavior of municipal solid waste landfill. The derivation of analytical solutions for specific conditions is introduced. The numerical approach, capable of coping with more general conditions, is also presented to estimate the spatial and temporal distribution of landfill settlement. The proposed model can simulate typical features of short- and long-term landfill settlement behaviors. With proper selection of parameter values, field measurements are well simulated by this model. The effects of some design parameters on the settlement behavior of municipal solid waste landfills are also examined with the help of this model.     

Consolidation in spatially random unsaturated soils based on coupled flow‐deformation simulation

This paper integrates random field simulation of soil spatial variability with numerical modeling of coupled flow and deformation to investigate consolidation in spatially random unsaturated soil. The spatial variability of soil properties is simulated using the covariance matrix decomposition method. The random soil properties are imported into an interactive multiphysics software COMSOL to solve the governing partial differential equations. The effects of the spatial variability of Young's modulus and saturated permeability together with unsaturated hydraulic parameters on the dissipation of excess pore water pressure and settlement are investigated using an example of consolidation in a saturated‐unsaturated soil column because of loading. It is found that the surface settlement and the pore water pressure profile during the process of consolidation are significantly affected by the spatially varying Young's modulus. The mean value of the settlement of the spatially random soil is more than 100% greater than that of the deterministic case, and the surface settlement is subject to large uncertainty, which implies that consolidation settlement is difficult to predict accurately based on the conventional deterministic approach. The uncertainty of the settlement increases with the scale of fluctuation because of the averaging effect of spatial variability. The effects of spatial variability of saturated permeability k_sat and air entry parameters are much less significant than that of elastic modulus. The spatial variability of air entry value parameters affects the uncertainties of settlement and excess pore pressure mostly in the unsaturated zone. Copyright © 2016 John Wiley & Sons, Ltd.     

Optimization of site exploration program for improved prediction of tunneling-induced ground settlement in clays

Excessive settlement caused by tunneling during construction often damages adjacent infrastructures and utilities. Such excessive settlement can also present a challenge in the maintenance of subways during their operation. Thus, it is important to be able to accurately predict tunneling-induced settlement. The uncertainties in geotechnical parameters, however, can lead to either an overestimation or an underestimation of the tunneling-induced settlement. Such uncertainties can arise from many sources such as spatial variability, measurement error, and model error; in this paper, the focus is on the geotechnical parameters characterization from site exploration. The goal here is to determine an optimal level of site exploration effort so that effective predictions of the tunneling-induced settlement in clays can be achieved. To this end, a Monte Carlo simulation-based numerical model of site exploration is first established to generate artificial test data. Then, a series of parametric analyses are performed to investigate the relationship between the level of site exploration effort and the accuracy of the tunneling-induced ground settlement prediction. Through the assumed different levels of site exploration effort, statistics of soil parameters are estimated using the maximum likelihood method and the tunneling-induced ground settlement is then analyzed using the probabilistic method, and finally the effect of site exploration effort is assessed. The knowledge generated from this series of analyses is then used to develop the proposed framework for selecting an optimal site exploration program for improved prediction of the tunneling-induced ground settlement in clays. Examples are presented to illustrate the proposed framework and demonstrate its effectiveness and significance.     

Applying Bayesian approach to predict deformations during tunnel construction

In this paper a fully probabilistic approach based on the Bayesian statistical method is presented to predict ground settlements in both transverse and longitudinal directions during gradual excavation of a tunnel. To that end, the convergence confinement method is adopted to give estimates of ground deformation numerically. Together with in situ measurements of the evolution of vertical deflections at selected points along the tunnel line, it allows for the construction of a likelihood function and consequently in the framework of Bayesian inference to provide posterior improved knowledge of model parameters entering the numerical analysis. In this regard, the Bayesian updating is first exploited in the material identification step and next used to yield predictions of ground settlement in sections along the tunnel line ahead of the tunnel face. This methodology thus makes it possible to improve original designs by utilizing an increasing number of data (measurements) collected in the course of tunnel construction.     

机场高填方蠕变沉降的数值反演预测

将考虑时间效应UH模型编写用户材料子程序（UMAT）并嵌入到有限元软件ABAQUS中,再结合多岛遗传算法搭建了数值反演平台。在实际工程中降雨、融雪和临时堆载等因素都会扰乱常态蠕变的沉降规律,使得沉降加快形成突变沉降,进而影响数值反演的预测结果。通过引入非常态蠕变概念并分析突变期间沉降加速的机制,提出了考虑非常态蠕变的反演预测方法。此外,当填方体缺乏分区依据且以多个监测点的沉降数据作为反演目标时,引入了反演参数的分布函数来代替几何模型的分区。并在此基础上进一步考虑沉降突变期间的非常态蠕变影响,进而提出多监测点下考虑非常态蠕变的沉降预测方法。最后,通过河北省承德机场高填方含有沉降突变的监测数据,验证了多监测点数据下考虑非常态蠕变的数值反演的准确性和实用性。     

大变形有效应力分析退化为总应力分析的新方法

传统方法在将有效应力分析向总应力分析退化时,一般采用将所有节点的孔压置为0的办法,这相当于对所有节点引入0压边界条件而影响了程序的通用性。将广义Terzaghi有效应力原理引入大变形固结理论中,并推导了其增量有限元方程。提出将广义有效应力系数置为0的新方法,实现了大变形有效应力分析向总应力分析的退化。新方法概念清晰,操作简单,不需要修改边界条件,可增强大变形有效应力分析程序的通用性。通过算例分析,证明了新方法的有效性。应用中可分别将广义有效应力系数置为1和0,对同一模型经过2次运算后,可对任意点在任意加荷条件下的固结沉降、最终沉降、固结度等指标进行准确评估,为工程设计和施工控制提供有效的分析手段。     

Settlement analysis of rectangular piles in nonhomogeneous soil using a Winkler model approach

An analytical approach using a Winkler model is investigated to provide analytical solutions of settlement of a rectangular pile subjected to vertical loads in nonhomogeneous soils. For a vertically loaded pile with a rectangular cross section, the settlement influence factor of a normal pile in nonhomogeneous soils is derived from Mindlin's solution for elastic continuum analysis. For short piles with rectangular and circular cross sections, the modified forms of settlement influence factors of normal piles are produced taking into account the load transfer parameter proposed by Randolph for short circular piles. The modulus of subgrade reaction along a rectangular pile in nonhomogeneous soils is expressed by using the settlement influence factor related to Mindlin's solution to combine the elastic continuum approach with the subgrade‐reaction approach. The relationship between settlement and vertical load for a rectangular pile in nonhomogeneous soils is available in the form of the recurrence equation. The formulation of settlement of soils surrounding a rectangular pile subjected to vertical loads in nonhomogeneous soils is proposed by taking into account Mindlin's solution and both the equivalent thickness and the equivalent elastic modulus for layers in the equivalent elastic method. The difference of settlement between square and circular piles is insignificant, and the settlement of a rectangular pile decreases as the aspect ratio of the rectangular pile cross section increases. The comparison of results calculated by the present method for a rectangular pile in nonhomogeneous soils has shown good agreement with those obtained from the analytical methods and the finite element method. Copyright © 2014 John Wiley & Sons, Ltd.     

The Bayesian bridge between simple and universal kriging

Kriging techniques are suited well for evaluation of continuous, spatial phenomena. Bayesian statistics are characterized by using prior qualified guesses on the model parameters. By merging kriging techniques and Bayesian theory, prior guesses may be used in a spatial setting. Partial knowledge of model parameters defines a continuum of models between what is named simple and universal kriging in geostatistical terminology. The Bayesian approach to kriging is developed and discussed, and a case study concerning depth conversion of seismic reflection times is presented.     

Forecast of creep settlements of heavy structures using pressuremeter tests

This paper presents a new method proposed for the prediction of long term settlement of very heavy structures based on a numerical interpretation of long term pressuremeter creep tests. The constitutive model used for the modelling of the soil creep is described. The constants of the constitutive model are determined by fitting a simulated curve, given by a simple Finite Element Method, to the results of a long term pressuremeter creep test. The calculation of long term settlement under nuclear power plants, using the soil parameters defined by this method, is presented and compared with the in situ measured values.     

基于原位试验成果的地基非线性沉降分析

提出了利用小尺寸载荷试验（PLT）及标准贯入试验（SPT）成果准确求解地基非线性沉降的新方法。首先利用压板载荷试验成果经过双曲线拟合,确定出某一土层地基土的修正切线模量;然后根据不同深度的SPT标贯击数按照线性相关关系,确定该土层不同深度各分层土的修正切线模量;最后依据分层总和法求解地基或基础的非线性沉降。该方法的特点是能进行加载至极限状态的全过程非线性沉降分析,而计算原理简单,计算参数全部来自现场原位测试,附加应力依据压板试验成果进行自动修正。分析广东某大型油罐地基及某高层建筑筏板基础的沉降结果表明,该方法计算结果准确可靠,能满足以沉降控制为目标的地基基础工程设计要求。     

Stochastic model for settlement: footings on cohesionless soil

In this paper, given an estimate of the bearing capacity of the soil, by treating settlement at a given load as a random variable and the evolution of settlement of footing on cohesionless soil with the increasing load as a stochastic process, a tri-level homogeneous Markov chain (TLHMC) model is proposed for prediction of settlement. Comparison of the predicted mean and bounds on settlements, obtained using TLHMC, with the respective field values obtained from literature shows that the stochastic evolution can be modelled using TLHMC with a correlation coefficient of 0.90. A methodology for reliability-based design of footings is also presented and its use is demonstrated through a numerical example.     

Learning soil parameters and updating geotechnical reliability estimates under spatial variability – theory and application to shallow foundations

ABSTRACT

Field data is commonly used to determine soil parameters for geotechnical analysis. Bayesian analysis allows combining field data with other information on soil parameters in a consistent manner. We show that the spatial variability of the soil properties and the associated measurements can be captured through two different modelling approaches. In the first approach, a single random variable (RV) represents the soil property within the area of interest, while the second approach models the spatial variability explicitly with a random field (RF). We apply the Bayesian concept exemplarily to the reliability assessment of a shallow foundation in a silty soil with spatially variable data. We show that the simpler RV approach is applicable in cases where the measurements do not influence the correlation structure of the soil property at the vicinity of the foundation. In other cases, it is expected to underestimate the reliability, and a RF model is required to obtain accurate results.     

软土路基沉降实时建模动态预测

在路基填筑施工过程中,用多项式与时间序列AR组合模型预测其沉降变形发展。根据沉降观测值,采用统计分析方法识别和建立多项式预测模型,预测在某时期沉降趋势值;用平稳时间序列分析方法建立随机部分模型,并预测沉降随机部分值,二者之和即为某时期沉降预测值。随着新观测数据的不断加入,及时修改预测模型参数值,达到实时预测之目的。工程实例研究表明：组合模型预测值明显优于单一趋势模型预测值。组合模型一步预测误差绝对值大多数情况下小于5 mm。预测步数越多,预测误差则越大。     

Modeling tunneling-induced ground surface settlement development using a wavelet smooth relevance vector machine

Accurate prediction of ground surface settlement is necessary for effectively controlling the settlement that develops during tunneling. Many models have been established for this purpose by extracting the relationship between the settlement and the factors that influence it. However, most of the models focused on the maximum ground surface settlement and do not involve dynamic and real-time predictions. This paper investigated how tunneling-induced ground surface settlement developed using a smooth relevance vector machine with a wavelet kernel (wsRVM). Various factors that affect this settlement, including geometrical, geological and shield operational parameters were considered. The model was applied to earth pressure balance (EPB) shield-driven tunnels. The results indicate that the prediction model performs well and that the distribution of the predictions can provide a measure of the prediction uncertainty. Unlike conventional methods that requireadditional efforts to determine relevant model parameters, the proposed method can optimize the parameters in the training process. The results of the parametric study conducted show that the model performance can be improved by the optimization and that the method can serve as a simple tool for practitioners to use in estimating ground surface settlement development during tunneling.     

Estimation of regionalized compositions: A comparison of three methods

A regionalized composition is a random vector function whose components are positive and sum to a constant at every point of the sampling region. Consequently, the components of a regionalized composition are necessarily spatially correlated. This spatial dependence—induced by the constant sum constraint—is a spurious spatial correlation and may lead to misinterpretations of statistical analyses. Furthermore, the cross-covariance matrices of the regionalized composition are singular, as is the coefficient matrix of the cokriging system of equations. Three methods of performing estimation or prediction of a regionalized composition at unsampled points are discussed: (1) the direct approach of estimating each variable separately; (2) the basis method, which is applicable only when a random function is available that can he regarded as the size of the regionalized composition under study; (3) the logratio approach, using the additive-log-ratio transformation proposed by J. Aitchison, which allows statistical analysis of compositional data. We present a brief theoretical review of these three methods and compare them using compositional data from the Lyons West Oil Field in Kansas (USA). It is shown that, although there are no important numerical differences, the direct approach leads to invalid results, whereas the basis method and the additive-log-ratio approach are comparable.     

基于饱和渗透系数空间变异结构的斜坡渗流及失稳特征

以往研究一般采用单随机变量方法（SRV）或基于水平或垂直方向波动范围生成的空间变异随机场来模拟岩土参数的空间变异性,对具有倾斜定向特征的空间变异随机场未有涉及.基于条件模拟相关理论和非侵入式随机有限元的理论框架,提出了利用序贯高斯模拟方法进行斜坡参数条件随机场模拟并运用有限元方法进行斜坡渗流和稳定性分析的方法.针对理想边坡,对各向同性和几何各向异性的共7种空间变异结构的饱和渗透系数（K_s）各进行了200次条件随机场模拟,基于条件随机场模拟结果进行了有限元渗流和稳定性计算,对每种空间变异结构多次计算结果进行了统计分析.结果表明:本文所提出的方法不仅再现了研究区域参数的空间二阶统计特性,通过设定变异函数参数进行不同空间变异类型、变异程度、变异定向性的随机场模拟,同时利用现场观测数据对随机场模拟结果进行条件限制,从而提高了随机场的赋值精度;K_s的空间变异结构对孔隙水压力的分布规律、地下水位线变化范围、稳定性系数和最危险滑动面分布特征均有一定程度的影响.本研究为库岸斜坡稳定性评价提供方法支撑.      

基于动态空间面板模型的填方路基边坡坡面位移预测

传统的边坡位移预测由于监测周期长、主要解释变量难以独立监测,存在建模困难、模型冗余、不具备空间预测能力等问题。以某半填半挖的高填方路堤边坡为研究对象,基于坡面位移实测数据,引入空间计量经济学基本理论和动态面板数据分析方法,检验和量化了不同测点间的空间关系,建立了坡面位移的动态空间面板数据模型,并进一步地检验了模型的预测结果。研究结果表明,相较于传统的时间序列模型,该模型参数更加简洁,并可同时对空间所有测点进行预测。模型不仅在时间维度有良好的预测效果,还具有一定的空间预测能力。     

Foundation settlement statistics via finite element analysis

The dispersion observed in soil data comes both from the spatial variability which greatly influences the behavior of large structures and from errors in testing. Thus, the geotechnical engineering deals with uncertainties for which deterministic approaches are not suitable. The resort to probabilistic techniques, enables modeling uncertainties by analyzing their dispersion effect on the global behavior of the structure. The scope of this paper is analyzing settlement and differential settlement variability of a pair of foundations on random heterogeneous medium. The random soil properties of interest are the elastic modulus, and the Poisson ratio. The elastic modulus is modeled as a spatially random field by adopting the lognormal distribution, which enables analyzing its large variability. Because soil Poisson ratio is bounded in practice between two extreme values, its random field is obtained by using the Beta distribution. In this study, one proposes for the Beta field determination, a mapping technique on the probability distribution function diagram, by solving a non-linear equation. However, the mean and variance are unchanged through the mapping operation. Because the soil Poisson ratio is a positive parameter, one prefers to perform the mapping operation with the probability function of the lognormal distribution. Also, the proposed technique can be used for other bounded soil properties such as the porosity. In this paper, settlement and differential settlement statistics prediction are carried out using Monte Carlo simulations combined with deterministic finite element method (DFEM). A performed parametric study shows the following: (i) as the variability of the elastic modulus increases as settlement and differential settlement statistics are important, also, settlement statistics decreases as the Poisson ratio variability increases, and differential settlement statistics do not seem be affected by its variability. (ii) settlement and differential settlement statistics are important for positive inter-property correlation. (iii) a great influence of the correlation lengths on settlement and differential settlement statistics.