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.     

The need for judgement in geotechnical reliability studies

ABSTRACT

A core principle of geotechnical engineering practice is the need to exercise judgement in evaluating soil and site conditions and in performing analyses. The requirement for considering judgement applies equally to performing deterministic or probabilistic analyses. In addition, for probabilistic analyses, choices must be made among the possible methods of analysis and the method of characterising variables. Consequently, geotechnical reliability studies inevitably involve significant uncertainties, and judgement is needed to perform reliability analyses and to evaluate the results. Two case histories, briefly described here, show that judgement and experience are essential prerequisites for meaningful assessment of geotechnical reliability.     

Full probabilistic design of slopes in spatially variable soils using simplified reliability analysis method

ABSTRACT

A 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.     

Markovian modeling of seismic damage accumulation

Analysis of civil structures at the scale of life‐cycle requires stochastic modeling of degradation. Phenomena causing structures to degrade are typically categorized as aging and point‐in‐time overloads. Earthquake effects are the members of the latter category this study deals with in the framework of performance‐based earthquake engineering (PBEE). The focus is structural seismic reliability, which requires modeling of the stochastic process describing damage progression, because of subsequent events, over time. The presented study explicitly addresses this issue via a Markov‐chain‐based approach, which is able to account for the change in seismic response of damaged structures (i.e. state‐dependent seismic fragility) as well as uncertainty in occurrence and intensity of earthquakes (i.e. seismic hazard). The state‐dependent vulnerability issue arises when the seismic hysteretic response is evolutionary and/or when the damage measure employed is such that the degradation increment probabilistically depends on the conditions of the structure at the time of the shock. The framework set up takes advantage also of the hypotheses of classical probabilistic seismic hazard analysis, allowing to separate the modeling of the process of occurrence of seismic shocks and the effect they produce on the structure. It is also discussed how the reliability assessment, which is in closed‐form, may be virtually extended to describe a generic age‐ and state‐dependent degradation process (e.g. including aging and/or when aftershock risk is of interest). Illustrative applications show the options to calibrate the model and its potential in the context of PBEE. Copyright © 2015 John Wiley & Sons, Ltd.     

边坡稳定分析的神经网络改进模糊点估计法

边坡稳定性分析中,模糊点估计法能同时考虑模糊不确定性和随机不确定性因素。针对传统模糊点估计法计算工作量大的缺点,提出一种神经网络改进模糊点估计法。利用拉丁超立方抽样法和径向基函数神经网络（RBF）建立边坡安全系数的预测模型;对黏聚力和内摩擦角等模糊随机变量取λ截集,并在各截集水平对参数进行组合;利用建立的预测模型对各参数组合的安全系数进行预测;最后由统计矩点估计法计算边坡的可靠度指标。实例分析表明：改进模糊点估计法使用方便、结果可靠,且能通过增加λ截集水平的数目来提高计算精度。对于含有2～4个模糊随机变量的边坡,采用改进模糊点估计法计算可靠度时λ截集水平的数目可近似取25。     

Seismic reliability assessment of urban water networks

We present a framework for the seismic risk assessment of water supply networks, operating in either normal or abnormal conditions. We propose a methodology for assessing the reliability of water pipe networks combining data of past non‐seismic damage and the vulnerability of the network components against seismic loading. Historical data are obtained using records of damages that occur on a daily basis throughout the network and are processed to produce‘survival curves’, depicting their estimated survival rate over time. The fragility of the network components is assessed using the approach suggested in the American Lifelines Alliance guidelines. The network reliability is assessed using graph theory, whereas the system network reliability is calculated using Monte Carlo simulation. The methodology proposed is demonstrated both on a simple, small‐scale, network and also on a real‐scale district metered area from the water network of the city of Limassol, Cyprus. The proposed approach allows the estimation of the probability that the network fails to provide the desired level of service and allows the prioritization of retrofit interventions and of capacity‐upgrade actions pertaining to existing water pipe networks. Copyright © 2013 John Wiley & Sons, Ltd.     

煤矿长斜井TBM施工安全风险分析与趋势预测

对煤矿长斜井盾构机（tunnel boring machine,TBM）施工的风险因素进行了识别,建立了二层次的风险评估指标体系,并确定了风险等级分类标准,利用熵权法确定风险指标的权向量,进而建立了基于集对分析法的煤矿长斜井TBM施工同异反评估模型。在此基础上利用偏联系数的理论确定了文中五元偏联系数的计算方法,根据改进的集对势理论给出风险趋势的预测方法。利用该模型对台格庙矿区煤矿长斜井（1#、2#实验井）TBM施工风险进行了评估与趋势预测。研究表明该模型与方法在煤矿长斜井TBM施工风险分析中是有效的、实用的,可为煤矿长斜井TBM施工风险分析与预测提供一种新的途径。     

盐岩地下储气库套管运行安全的可靠性分析研究

盐岩因其具有良好的蠕变特性、低渗透性以及损伤自我恢复特性成为国际上公认的最理想能源地下存储介质。在盐岩地下储气库运营过程中储库套管受到储气内压、材料参数和几何尺寸等不确定性因素的影响,为了评价这些随机风险因素对储库套管结构运行安全的影响,建立了盐岩储气库的套管结构模型和基于Von Mises屈服准则的套管结构功能函数,根据建立的结构模型和结构功能函数,应用响应面法结合蒙特卡洛抽样计算获得储库在高压和低压运行条件下套管结构可靠度的变化规律。可靠性分析表明,为保证套管运行安全,储库最低运行内压应大于3 MPa,最高运行内压应小于22 MPa,套管靴距离储库腔顶的距离应大于10 m,并应适当加大套管的壁厚和减小套管的内径。     

基于综合变异系数的地基承载力可靠性分析

采用一次二阶矩法,考虑岩土参量变异系数的试验及历史数据,提出了基于综合变异系数的地基承载力简化可靠性与风险分析方法。利用提出的简化可靠性与风险分析方法,可在传统确定性分析的基础上,采用合理的变异系数,分别得到与地基承载力有关的可靠度、破坏概率和平均期望损失的最可能值及其变化范围,为提出优化的地基基础设计方案和工程决策奠定基础;相对于土重度和黏聚力,地基承载力可靠度对于内摩擦角的变化更加敏感;设计时可以综合考虑所需的安全系数、可靠度和破坏概率,确定合适的基础宽度或基底面积。当地基土的场地勘察统计结果的变异系数权重逐渐增加时,综合变异系数不断减小,其相应的地基承载力的可靠度逐渐增加,而相应的破坏概率逐渐减少。     

溪洛渡电站软弱夹层剪切强度分析研究

岩体的力学强度参数,特别是软弱夹层剪切强度参数c、f的优化选取进行分析对比,对大型和特大型水电工程来说是非常必要的.本文采用多种力学参数选取方法讨论了溪洛渡水电站软弱夹层剪切强度参数c、f的取值问题,使剪切强度的选取有了更大的可比性,提高了c、f值的选取精度和可靠性.