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1.
This paper aims to develop an efficient geotechnical reliability-based design (RBD) approach using Monte Carlo simulation (MCS). The proposed approach combines a recently developed MCS-based RBD approach, namely expanded RBD approach, with an advanced MCS method called “Subset Simulation (SS)” to improve the computation efficiency at small probability levels that are often concerned in geotechnical design practice. To facilitate the integration of SS and expanded RBD, a generalized surrogate response f is proposed to define the driving variable, which is a key parameter in SS, for expanded RBD of geotechnical structures (e.g., soil retaining structures and foundations). With the aid of the proposed surrogate response, failure probabilities of all the possible designs in a prescribed design space are calculated from a single run of SS. Equations are derived for integration of the surrogate response-aided SS and expanded RBD, and are illustrated using an embedded sheet pile wall design example and two drilled shaft design examples. Results show that the proposed approach provides reasonable estimates of failure probabilities of different designs using a single run of the surrogate response-aided SS, and significantly improves the computational efficiency at small probabilities levels in comparison with direct MCS-based expanded RBD. The surrogate response-aided SS is able to, simultaneously, approach the failure domains of all the possible designs in the design space by a single run of simulation and to generate more complete design information, which subsequently yields feasible designs with a wide range of combinations of design parameters. This is mainly attributed to the strong correlation between the surrogate response and target response (e.g., factor of safety) of different designs concerned in geotechnical RBD. 相似文献
2.
在有限数据条件下,可靠度敏感性分析是研究各种不确定性因素对边坡失稳概率影响规律的重要途径。基于直接蒙特卡洛模拟和概率密度加权分析方法提出了一种高效边坡稳定可靠度敏感性分析方法。所提出的方法通过随机场表征岩土体参数的空间变异性,并采用局部平均理论建立岩土体参数的缩维概率密度函数,用于概率密度加权分析中高效、准确地计算不同敏感性分析方案对应的边坡失稳概率。最后,通过一个工程案例--詹姆斯湾堤坝说明了所提出方法的有效性和准确性。结果表明：在敏感性分析过程中,所提出的方法只需要执行一次直接蒙特卡洛模拟,避免了针对不同敏感性分析方案重新产生随机样本和执行边坡稳定分析,节约了大量的计算时间和计算资源,显著提高了基于蒙特卡洛模拟的敏感性分析计算效率;在概率密度加权分析中采用岩土体参数的缩维概率密度函数能够准确地计算边坡失稳概率,避免了有偏估计,使概率密度加权分析方法适用于考虑空间变异性条件下的边坡稳定可靠度敏感性分析问题。 相似文献
3.
Cone Penetration Test (CPT) is widely utilized to gain regular geotechnical parameters such as compression modulus, cohesion coefficient and internal friction angle by transformation model in the site investigation. However, it is challenging to obtain simultaneously the unknown coefficients and error of a transformation model, given the intrinsic uncertainty (i.e., spatial variability) of geomaterial and the epistemic uncertainty of geotechnical investigation. A Bayesian approach is therefore proposed calibrating the transformation model based on spatial random field theory. The approach consists of three key elements: (1) three-dimensional anisotropic spatial random field theory; (2) classifications of measurement and error, and the uncertainty propagation diagram of geotechnical investigation; and (3) the unknown coefficients and error calibration of the transformation model given Bayesian inverse modeling method. The massive penetration resistance data from CPT, which is denoted as a spatial random field variable to account for the spatial variability of soil, are classified as type A data. Meanwhile, a few laboratory test data such as the compression modulus are defined as type B data. Based on the above two types of data, the unknown coefficients and error of the transformation model are inversely calibrated with consideration of intrinsic uncertainty of geomaterial, epistemic uncertainties such as measurement errors, prior knowledge uncertainty of transformation model itself, and computing uncertainties of statistical parameters as well as Bayesian method. Baseline studying indicates the proposed approach is applicable to calibrate the transformation model between CPT data and regular geotechnical parameter within spatial random field theory. Next, the calibrated transformation model was compared with classical linear regression in cross-validation, and then it was implemented at three-dimensional site characterization of the background project. 相似文献
4.
This paper aims to investigate the impact of copula selection on geotechnical reliability under incomplete probability information. The copula theory is introduced briefly. Thereafter, four copulas, namely Gaussian, Plackett, Frank, and No. 16 copulas, are selected to model the dependence structure between cohesion and friction angle. A copula-based approach is used to construct the joint probability density function of cohesion and friction angle with given marginal distributions and correlation coefficient. The reliability of an infinite slope and a retaining wall is presented to demonstrate the impact of copula selection on reliability. The results indicate that the probabilities of failure of geotechnical structures with given marginal distributions and correlation coefficient of shear strength parameters cannot be determined uniquely. The resulting probabilities of failure associated with different copulas can differ considerably. Such a difference increases with decreasing probability of failure. Significant difference in probabilities of failure could be observed for relatively small coefficients of variation of the shear strength parameters or a strong negative correlation between cohesion and friction angle. The Gaussian copula, often adopted out of expedience without proper validation, may not capture the dependence structure between cohesion and friction angle properly. Furthermore, the Gaussian copula may greatly underestimate the probability of failure for geotechnical structures. 相似文献
5.
Shuo Zheng Yu-Xin Zhu Dian-Qing Li Zi-Jun Cao Qin-Xuan Deng Kok-Kwang Phoon 《地学前缘(英文版)》2021,12(1):425-439
Various uncertainties arising during acquisition process of geoscience data may result in anomalous data instances(i.e.,outliers)that do not conform with the expected pattern of regular data instances.With sparse multivariate data obtained from geotechnical site investigation,it is impossible to identify outliers with certainty due to the distortion of statistics of geotechnical parameters caused by outliers and their associated statistical uncertainty resulted from data sparsity.This paper develops a probabilistic outlier detection method for sparse multivariate data obtained from geotechnical site investigation.The proposed approach quantifies the outlying probability of each data instance based on Mahalanobis distance and determines outliers as those data instances with outlying probabilities greater than 0.5.It tackles the distortion issue of statistics estimated from the dataset with outliers by a re-sampling technique and accounts,rationally,for the statistical uncertainty by Bayesian machine learning.Moreover,the proposed approach also suggests an exclusive method to determine outlying components of each outlier.The proposed approach is illustrated and verified using simulated and real-life dataset.It showed that the proposed approach properly identifies outliers among sparse multivariate data and their corresponding outlying components in a probabilistic manner.It can significantly reduce the masking effect(i.e.,missing some actual outliers due to the distortion of statistics by the outliers and statistical uncertainty).It also found that outliers among sparse multivariate data instances affect significantly the construction of multivariate distribution of geotechnical parameters for uncertainty quantification.This emphasizes the necessity of data cleaning process(e.g.,outlier detection)for uncertainty quantification based on geoscience data. 相似文献
6.
Andrew Skabar 《Mathematical Geology》2007,39(5):439-451
Multilayer perceptrons (MLPs) can be used to discover a function which can be used to map from a set of input variables onto
a value representing the conditional probability of mineralization. The standard approach to training MLPs is gradient descent,
in which the error between the network output and the target output is reduced in each iteration of the training algorithm.
In order to prevent overfitting, a split-sample validation procedure is used, in which the data is partitioned into two sets:
a training set, which is used for weight optimization, and a validation set, which is used to optimize various parameters
that can be used to prevent overfitting. One of the problems with this approach is that the resulting maps can display significant
variability which stems from (i) the (randomly initialized) starting weights and (ii) the particular training/validation set
partition (also determined randomly). This problem is especially pertinent on mineral potential mapping tasks, in which the
number of deposit cells is a very small proportion of the total number of cells in the study area. In contrast to gradient
descent methods, Bayesian learning techniques do not find a single weight vector; rather, they infer the posterior distribution
of the weights given the data. Predictions are then made by integrating over this distribution. An important advantage of
the Bayesian approach is that the optimization of parameters such as the weight decay regularization coefficient can be performed
using training data alone, thus avoiding the noise introduced through split-sample validation. This paper reports results
of applying Bayesian learning techniques to the production of maps representing gold mineralization potential over the Castlemaine
region of Victoria, Australia. Maps produced using the Bayesian approach display significantly less variability than those
produced using gradient descent training. They are also more reliable at predicting the presence of unknown deposits. 相似文献
7.
This paper presents probabilistic assessment of seismically-induced slope displacements considering uncertainties of seismic ground motions and soil properties.A stochastic ground motion model representing both the temporal and spectral non-stationarity of earthquake shakings and a three-dimensional rotational failure mechanism are integrated to assess Newmark-type slope displacements.A new probabilistic approach that incorporates machine learning in metamodeling technique is proposed,by combining relevance vector machine with polynomial chaos expansions(RVM-PCE).Compared with other PCE methods,the proposed RVM-PCE is shown to be more effective in estimating failure probabilities.The sensitivity and relative influence of each random input parameter to the slope displacements are discussed.Finally,the fragility curves for slope displacements are established for sitespecific soil conditions and earthquake hazard levels.The results indicate that the slope displacement is more sensitive to the intensities and strong shaking durations of seismic ground motions than the frequency contents,and a critical Arias intensity that leads to the maximum annual failure probabilities can be identified by the proposed approach. 相似文献
8.
Monte Carlo Simulation (MCS) method has been widely used in probabilistic analysis of slope stability, and it provides a robust and simple way to assess failure probability. However, MCS method does not offer insight into the relative contributions of various uncertainties (e.g., inherent spatial variability of soil properties and subsurface stratigraphy) to the failure probability and suffers from a lack of resolution and efficiency at small probability levels. This paper develop a probabilistic failure analysis approach that makes use of the failure samples generated in the MCS and analyzes these failure samples to assess the effects of various uncertainties on slope failure probability. The approach contains two major components: hypothesis tests for prioritizing effects of various uncertainties and Bayesian analysis for further quantifying their effects. Equations are derived for the hypothesis tests and Bayesian analysis. The probabilistic failure analysis requires a large number of failure samples in MCS, and an advanced Monte Carlo Simulation called Subset Simulation is employed to improve efficiency of generating failure samples in MCS. As an illustration, the proposed probabilistic failure analysis approach is applied to study a design scenario of James Bay Dyke. The hypothesis tests show that the uncertainty of undrained shear strength of lacustrine clay has the most significant effect on the slope failure probability, while the uncertainty of the clay crust thickness contributes the least. The effect of the former is then further quantified by a Bayesian analysis. Both hypothesis test results and Bayesian analysis results are validated against independent sensitivity studies. It is shown that probabilistic failure analysis provides results that are equivalent to those from additional sensitivity studies, but it has the advantage of avoiding additional computational times and efforts for repeated runs of MCS in sensitivity studies. 相似文献
9.
双排钢板桩围堰的整体稳定性具有较大的不确定性,同样安全系数情况下对应的失稳概率可能不同。为了更准确地分析双排钢板桩围堰的整体稳定性,降低稳定性分析中的不确定性,采用贝叶斯方法对船坞双排钢板桩围堰的整体稳定性进行可靠度分析。首先通过统计数据获取土体参数的先验分布,然后基于实测数据采用贝叶斯方法更新参数以得到后验分布,最后根据参数的后验分布采用一次二阶矩计算围堰结构的可靠度。贝叶斯方法从理论的角度解决了已有工程经验和实际案例数据两方面信息有效综合的问题,能在更接近实际情况的前提下进行可靠度分析。 相似文献
10.
S. Sujitha Deepthi Mary Dilip 《Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards》2017,11(2):208-214
In the present study, reliability analysis of near surface disposal facility is performed, by assessing the probability of sequential failure of the multi barrier system using the contaminant transport model. The concentration and dose rate of the radionuclide evolve with time hence there is a need for time dependent reliability analysis. Due to the low values of expected probabilities of failure, an enhanced Monte Carlo (EMC) method and Subset simulation is employed. The Result of the analysis show that, the EMC method is useful to evaluate the probability of failure associated with the barrier system which has low probability of failure. 相似文献
11.
A new type of indirect inverse analysis procedure is proposed to overcome the difficulties the geotechnical inverse analyses are encountering (such as unstability and non-uniqueness of the solutions as well as multicollinearity). These difficulties are eased by combining the objective information (i.e. the observation data) and the subjective information (i.e. the prior information) in an appropriate manner by so-called extended Bayesian method. The method is based on a new view on Bayesian model proposed by Akaike. The problem of model identification in the inverse analysis is also tackled by applying well-known AIC but of the Bayesian version. A case study on an embankment on soft clay is presented to illustrate the effectiveness of the new method. A rather thorough review on the geotechnical inverse analysis is also presented to indicate the necessity of the proposed procedure. An appendix is attached to summarize the statistical background of the new method. 相似文献
12.
基于综合变异系数的地基承载力可靠性分析 总被引：2，自引：0，他引：2
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。 相似文献
13.
The first order reliability method (FORM) is efficient, but it has limited accuracy; the second order reliability method (SORM) provides greater accuracy, but with additional computational effort. In this study, a new method which integrates two quasi-Newton approximation algorithms is proposed to efficiently estimate the second order reliability of geotechnical problems with reasonable accuracy. In particular, the Hasofer–Lind–Rackwitz–Fiessler–Broyden–Fletcher–Goldfarb–Shanno (HLRF–BFGS) algorithm is applied to identify the design point on the limit state function (LSF), and consequently to compute the first order reliability index; whereas the Symmetric Rank-one (SR1) algorithm is nested within the HLRF–BFGS algorithm to compute good approximations, yet with a reduced computational effort, of the Hessian matrix required to compute second order reliabilities. Three typical geotechnical problems are employed to demonstrate the ability of the suggested procedure, and advantages of the proposed approach with respect to conventional alternatives are discussed. Results show that the proposed method is able to achieve the accuracy of conventional SORM, but with a reduced computational cost that is equal to the computational cost of HLRF–BFGS-based FORM. 相似文献
14.
At geotechnical sites, deformation measurements are routinely performed during the construction process. In this paper, it is shown how information from such measurements can be utilized to update the reliability estimate of the geotechnical site at future construction stages. A recently proposed method for Bayesian updating of the reliability is successfully applied in conjunction with a stochastic nonlinear geotechnical finite element model. Therein, uncertainty in the soil material properties is modelled by non-Gaussian random fields. The structural reliability evaluations required for the Bayesian updating are carried out by means of subset simulation, an efficient adaptive Monte Carlo method. The approach is demonstrated through an application to a sheet pile wall at a deformation-sensitive geotechnical construction site. 相似文献
15.
Geotechnical modeling at the city scale using statistical and geostatistical tools: The Pessac case (France) 总被引：3，自引：0，他引：3
In the context of the RIVIERA project, the building of a 3D geotechnical model at the city scale (Pessac, France) has been undertaken, from several hundreds of boreholes and geotechnical tests. It is first shown how the combination of the lithological information and of geotechnical results can improve thanks to Bayesian statistics the knowledge of mechanical characteristics in the various alluvial terraces which can be encountered in this area. Secondly the upper and lower limits of the 3D model at the city scale are computed by improving an initial digital elevation model for the upper limit and by kriging under inequality constraints for the lower limit. These limits border Quaternary formations which are of interest for geotechnical applications. In a third stage, it is focused on the spatial modelling of the pressuremeter modulus. The sequential indicator simulation method enables to obtain the spatial probability of occurrence of a given pressiometer modulus class. Coupled with other information, the analysis of these statistical and geostatistical models makes possible to develop decision support tools such as to localise, for instance, areas more prone to the clay shrinkage–swelling hazard. 相似文献
16.
高斯过程回归（GPR）是一种基于贝叶斯理论的监督学习算法,在基于数据驱动（DDM）的模型结构不确定性分析中具有广泛应用。目前研究中通常假设物理参数和超参独立并进行联立识别,这会导致参数补偿。文章提出两步识别DDM量化模型结构误差,并通过2个地下水模型案例,分别在不考虑模型结构误差、考虑模型结构误差（联立识别DDM、两步识别DDM）的情况下,对比分析了参数识别和模型预测结果。结果表明,不考虑模型结构误差直接进行参数识别时,为补偿结构误差,物理参数会过度拟合,从而影响模型预测效果。基于DDM刻画模型结构偏差时,物理参数和超参的独立性假设会影响参数识别结果。提出的两步识别DDM法没有假设物理参数和超参独立,能够减少参数过度拟合效应,从而更准确刻画结构误差,有效提高了模型的预测性能。 相似文献
17.
对于超固结黏土和密实砂土等软化材料或非关联塑性材料组成的地基、边坡及挡土墙墙后土体,在其破坏过程中,会产生应变局部化现象,使得控制方程的类型发生改变,从而导致出现数值解不惟一和解的网格相关性等现象。为了克服这些数值困难,基于强间断分析方法,及单元内嵌不连续面的有限元模型,对地基、土坡、墙后土体的渐进破坏过程进行了数值模拟。计算结果表明,单元内嵌不连续面模型可以有效地模拟土工结构失稳破坏过程,并且能够明显地改善采用常规有限元方法所产生的网格尺寸相关性问题。这一方法可作为传统极限平衡法进行稳定分析、承载力分析的有益补充。 相似文献
18.
针对统一强度理论（UST）拟合非线性强度的局限和岩土材料强度特点,提出拟合岩土非线性强度特征的脆性剪切分析方法,建立了过渡式双线性强度分解公式。以脆断、剪切双重破坏机制,解释Hoek-Brown准则的非线性特征机制,并推导出体现岩石、岩体类材料非线性强度特征的脆剪强度公式。Mine-by隧道和北山花岗岩强度数据分析显示,在岩体折减强度、试验数据直接拟合实例分析中,脆剪强度与Hoek-Brown强度相关性系数均在0.98以上,在该基础上建立岩体非线性强度脆剪分析的双线性过渡式,并提出对应的统一强度理论形式,实现统一强度理论在拟合岩土非线性强度准则中的扩展。 相似文献
19.
To solve large deformation geotechnical problems, a novel strain-smoothed particle finite element method (SPFEM) is proposed that incorporates a simple and effective edge-based strain smoothing method within the framework of original PFEM. Compared with the original PFEM, the proposed novel SPFEM can solve the volumetric locking problem like previously developed node-based smoothed PFEM when lower-order triangular element is used. Compared with the node-based smoothed PFEM known as “overly soft” or underestimation property, the proposed SPFEM offers super-convergent and very accurate solutions due to the implementation of edge-based strain smoothing method. To guarantee the computational stability, the proposed SPFEM uses an explicit time integration scheme and adopts an adaptive updating time step. Performance of the proposed SPFEM for geotechnical problems is first examined by four benchmark numerical examples: (a) bar vibrations, (b) large settlement of strip footing, (c) collapse of aluminium bars column, and (d) failure of a homogeneous soil slope. Finally, the progressive failure of slope of sensitive clay is simulated using the proposed SPFEM to show its outstanding performance in solving large deformation geotechnical problems. All results demonstrate that the novel SPFEM is a powerful and easily extensible numerical method for analysing large deformation problems in geotechnical engineering. 相似文献
20.
The Bayesian approach is an effective method of identifying the probability of mineralogical and geochemical type (MGT) mineralization of trace elements in galena, pyrite and other distributions in ore mineralization. Monomineralic samples have been identified using a computer-based Bayesian method and exploration geochemical techniques of Au deposits for MGT. In order to employ the method, a data bank was used consisting of the results of analysis of more than 12,000 monomineralic samples collected from the main hydrothermal Au deposits in Tajikistan (a territory of CIS). The Bayesian approach applied to geochemical data, such as posterior probabilities and discriminant analysis, provide numerical and graphical means through which the relationships between the trace elements and samples can be studied. The method used here, along with GIS, to find MGT can be used as geochemical indicators of regions with Au mineralization. The results of analyzing 100 monomineralic samples of pyrite from the Au–Ag Shkolnoe deposit (Tajikistan) show a multi-MGT anomaly superposition which is a combination of three MGT: (1) Au–Ag type (85% and more), (2) Au–sulfide-polymetallic type (46%), and (3) Au–sulfide type (40%). Mineralogical and geochemical maps (MGM) can be drawn based on results of MGT anomalies in a GIS environment. These maps can replace traditional metallogenic maps. The advantage of MGM substitutions is that a qualitative tool is replaced by a quantitative one. This helps one to make optimal managerial and more economical decisions. 相似文献