共查询到20条相似文献，搜索用时 15 毫秒
1.
受工程勘察成本及试验场地限制,可获得的试验数据通常有限,基于有限的试验数据难以准确估计岩土参数统计特征和边坡可靠度。贝叶斯方法可以融合有限的场地信息降低对岩土参数不确定性的估计进而提高边坡可靠度水平。但是,目前的贝叶斯更新研究大多假定参数先验概率分布为正态、对数正态和均匀分布,似然函数为多维正态分布,这种做法的合理性有待进一步验证。总结了岩土工程贝叶斯分析常用的参数先验概率分布及似然函数模型,以一个不排水黏土边坡为例,采用自适应贝叶斯更新方法系统探讨了参数先验概率分布和似然函数对空间变异边坡参数后验概率分布推断及可靠度更新的影响。计算结果表明：参数先验概率分布对空间变异边坡参数后验概率分布推断及可靠度更新均有一定的影响,选用对数正态和极值I型分布作为先验概率分布推断的参数后验概率分布离散性较小。选用Beta分布和极值I型分布获得的边坡可靠度计算结果分别偏于保守和危险,选用对数正态分布获得的边坡可靠度计算结果居中。相比之下,似然函数的影响更加显著。与其他类型似然函数相比,由多维联合正态分布构建的似然函数可在降低对岩土参数不确定性估计的同时,获得与场地信息更为吻合的计算结果。另外,构建似然函数时不同位置处测量误差之间的自相关性对边坡后验失效概率也具有一定的影响。 相似文献
2.
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
3.
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
4.
在贝叶斯理论框架下,根据一种可结合多个水文模型给出模拟或预报结果的IBUNE方法探讨了水文模型的输入、参数以及结构的不确定性问题。将SCEM-UA算法和EM算法嵌入新安江和TOPMODEL水文模型用于参数优化和模型平均,进而将输入与参数的综合不确定性处理后得到的预报量后验分布进行多模型综合,据此对水文模型的不确定性及其对水文模拟结果的影响进行评价。以湖南洣水流域龙家山水文站以上集水区域为例进行了应用研究,结果表明,IBUNE方法能够有效估计水文模型的不确定性,并能给出合理的概率预报区间。 相似文献
5.
利用大样本数据掌握土工参数的变异性特征,可为岩土工程可靠度分析中计算指标选取提供客观依据,提高分析结果准确性。收集了国内不同地域6条铁路勘察近千个试样的土工试验数据,经工程地质单元的聚类分析验证,建立了13组土样大样本,采用概率统计方法分析了土体强度参数c、φ的分布特征及变异系数变化规律,基于灰色理论讨论了强度参数变异水平受土体物理力学指标影响的关联性排序。研究表明:勘察工点地基土体强度参数变异系数δc、δφ均普遍处于大变异水平,且两者间呈现协同变化的正相关规律;反映土体状态特性的液性指数IL、含水率w、压缩系数a1-2以及天然孔隙比e与δc、δφ系数间的关联性强于体现土质自身性质的塑性指数IP,其中,IL是影响δc、δφ的首位因素,a1-2和w分别为次位影响因素。得到的强度参数变异水平与影响因素关联特征,可为样本数量有限下土体强度参数的不确定性判断提供先验信息。 相似文献
6.
Iason Papaioannou Daniel Straub 《Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards》2017,11(1):116-128
ABSTRACTField 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. 相似文献
7.
地基沉降修正系数的Bayes概率推断 总被引：2，自引：0，他引：2
通过分析常规方法在沉降修正系数的选取中具有的定值性和随意性,引入建立在过去信息和现在样本信息之上的Bayes理论,结合某客运专线红黏土路基工程,提出用后验分布得到修正系数的取值范围。实例研究表明,用以往经验综合样本信息,估计修正系数的先验概率在某一区间上服从均匀分布。由现场载荷试验实测沉降量与理论计算沉降量分析所得的修正系数,将现场量测的沉降变形信息与先验信息结合起来,利用Bayes统计理论,由小样本试验数据推算得到修正系数的后验概率服从正态分布。对后验分布所得参数进行区间估计,得到该区域红黏土地基沉降修正系数的取值优化区间为 [1.0, 1.7],分析了不同荷载作用条件下沉降修正系数的概率分布模型。 相似文献
8.
Mine gas disaster prediction and prevention are based on gas content measurement, which results in initial stage loss when determining coal gas desorption contents in engineering applications. We propose a Bayesian probability statistical method in the coal gas desorption model on the basis of constrained prior information. First, we use a self-made coal sample gas desorption device to test initial stage gas desorption data of tectonic coal and undeformed coal. Second, we calculate the initial stage loss of different coal samples with the power exponential function parameters by using Bayesian probability statistics and least squares estimation. Results show that Bayesian probability statistics and least squares estimation can be used to obtain regression and desorption coefficients, thereby illustrating the Bayesian estimation method’s validity and reliability. Given that the Bayesian probability method can apply prior information to constrain the model’s posterior parameters, it provides results that are statistically significant in the initial stage loss of coal gas desorption by connecting observation data and prior information.
相似文献9.
岩土工程现场勘察试验通常只能获得有限的试验数据,据此难以真实地量化土体参数的空间变异性。提出了考虑土体参数空间变异性的概率反演和边坡可靠度更新方法,基于室内和现场两种不同来源的试验数据概率反演空间变异参数统计特征和更新边坡可靠度水平,并给出了计算流程。此外为合理地描述土体参数先验信息,发展了不排水抗剪强度非平稳随机场模型。最后通过不排水饱和黏土边坡算例验证了提出方法的有效性,并探讨了试验数据和钻孔位置对边坡后验失效概率的影响。结果表明：提出方法实现了空间变异土体参数概率反演与边坡可靠度更新的一体化,基于有限的多源试验数据概率反演得到的土体参数均值与试验数据非常吻合,明显降低了对参数不确定性的估计,更新的边坡可靠度水平显著增加。受土体参数空间自相关性的影响,试验数据对钻孔取样点附近区域土体参数统计特征更新的影响明显大于距离取样点较远区域。 相似文献
10.
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
11.
Wei Yang Yun Xu 《Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards》2017,11(2):189-196
The Bayesian approach has been proved useful to geotechnical engineering especially when project-specific data/measurements are very limited. In this paper, we introduced a new Bayesian algorithm to estimate soil properties at each location of a study area, with very limited project-specific data. In addition to the proposed methodology and algorithms, we also conducted a model application to estimate soil permeability for each of the 64 locations within a 4-by-4 m2 area, based on very limited project-specific data, that is, one measurement from each location. 相似文献
12.
《地学前缘(英文版)》2018,9(6):1609-1618
Rock properties exhibit spatial variabilities due to complex geological processes such as sedimentation,metamorphism, weathering, and tectogenesis. Although recognized as an important factor controlling the safety of geotechnical structures in rock engineering, the spatial variability of rock properties is rarely quantified. Hence, this study characterizes the autocorrelation structures and scales of fluctuation of two important parameters of intact rocks, i.e. uniaxial compressive strength(UCS) and elastic modulus(EM).UCS and EM data for sedimentary and igneous rocks are collected. The autocorrelation structures are selected using a Bayesian model class selection approach and the scales of fluctuation for these two parameters are estimated using a Bayesian updating method. The results show that the autocorrelation structures for UCS and EM could be best described by a single exponential autocorrelation function. The scales of fluctuation for UCS and EM respectively range from 0.3 m to 8.0 m and from 0.3 m to 8.4 m.These results serve as guidelines for selecting proper autocorrelation functions and autocorrelation distances for rock properties in reliability analyses and could also be used as prior information for quantifying the spatial variability of rock properties in a Bayesian framework. 相似文献
13.
在重要的岩土工程中,确定大样本岩土参数的概率分布对岩土工程稳定性和可靠性分析有着极其重要的意义。为此,基于积分均方误差最小计算得到的最优窗宽,提出了推断大样本岩土参数概率密度函数的正态信息扩散法。该方法基于信息扩散原理,从试验样本和信息论的角度出发,充分利用样本提供的数据信息,而不是先假定成经典的概率分布曲线拟合检验,数学意义和物理意义更加充分和严密。以推断压缩指数Cc的概率密度函数为例,分析了窗宽对信息扩散估计结果的影响规律,说明了该方法在大样本岩土参数概率密度函数推断方面的合理性。用该方法推断了大样本岩土抗剪强度参数的概率密度函数,通过K-S检验,验证了该方法的正确性和实用性。 相似文献
14.
基于贝叶斯理论的灌注桩多个缺陷统计特性分析 总被引：1，自引：0，他引：1
由于施工技术水平、岩土工程条件等不确定性因素的影响,基桩中经常出现各种缺陷。为此,提出了基于贝叶斯理论的灌注桩多个缺陷统计特性的分析方法。在考虑钻芯法检测不确定性的基础上,采用泊松分布模型模拟基桩中多个缺陷的出现概率,推导了缺陷平均出现率后验分布的计算公式。提出了估计缺陷尺寸修正的贝叶斯抽样方法,给出了评价钻芯法检测概率的方法。算例分析表明,钻芯法的检测概率对准确地估计缺陷平均出现率有明显的影响,如果不考虑检测不确定性因素的影响,缺陷平均出现率将被低估。随着检测到缺陷数目的增加,更新的缺陷平均出现率的均值逐渐增加,更新的变异系数逐渐减小。此外,先验的信息能够有效地减小缺陷平均出现率和缺陷尺寸估计的不确定性。 相似文献
15.
火山碎屑岩岩性的测井识别方法 总被引：4，自引：2，他引：4
火成岩岩性识别是岩相划分、储层评价及开发方案编制的基础。针对火成岩岩石类型多、测井识别难度大问题,应用贝叶斯判别分析方法对火山碎屑岩测井解释中岩性识别问题进行研究。通过对样本集的优化处理和先验概率的应用,提高了识别率。应用该方法对海拉尔盆地乌尔逊凹陷乌东地区火山碎屑岩储层进行了岩性自动识别,符合率达到80%以上。 相似文献
16.
某些岩土参数的对数正态分布特征 总被引：1，自引：0，他引：1
本文试图说明服从对数正态分布岩土参数频率直方图的分布特征和在累积频率概率格约牙作正态检验及求取参数特征值的统计方法。使岩土参数的统计更合理。 相似文献
17.
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
18.
Worldwide, there is growing interest in the development of a rational reliability-based geotechnical design code. The reasons for this interest are at least two-fold; first, geotechnical engineers face significantly more uncertainties than those faced in other fields of engineering, therefore there is a need to properly characterize and deal with these uncertainties. Second, for decades, structural engineers have used a reliability-based design code, and there is a need to develop the same for geotechnical engineers, in order that the two groups can ‘speak the same language’. This paper develops a theoretical model to predict the probability that a shallow foundation will exceed its supporting soil's bearing capacity. The footing is designed using characteristic soil properties (cohesion and friction angle) derived from a single sample, or ‘core’, taken in the vicinity of the footing, and used in a load and resistance factor design approach. The theory predicting failure probability is validated using a two-dimensional random finite element method analysis of a strip footing. Agreement between theory and simulation is found to be very good. Therefore, the theory can be used with confidence to perform risk assessments of foundation designs and develop resistance factors for use in code provisions. 相似文献
19.
Polynomial chaos expansions (PCEs) have been widely employed to estimate failure probabilities in geotechnical engineering. However, PCEs suffer from two deficiencies: (a) PCE coefficients are solved by the least-square minimization method which easily causes overfitting issues; (b) building a high order PCE is often computationally expensive. In order to overcome the aforementioned drawbacks, the Bayesian regression technique is employed to evaluate PCE coefficients, which not only provides a sparse solution but also avoids overfitting. With the aid of the predictive means and variances given by Bayesian analysis, a learning function is proposed to sequentially select the most informative samples that are critical to build a PCE. This sequential learning scheme can highly enhance the computational efficiency of PCEs. Besides, importance sampling (IS) is incorporated into the sequential learning (SL)-PCEs to deal with geotechnical problems with small failure probabilities. The proposed method of SL-PCE-IS is applied to three illustrative examples, which shows that the improved PCE method is more effective and efficient than the common PCEs method, leading to accurate estimations of small failure probabilities using fewer training samples. 相似文献
20.
Accurate estimation of geotechnical parameters is an important and difficult task in tunnel design and construction. Optimum evaluation of the geotechnical parameters have been carried out by the back‐analysis method based on estimated absolute convergence data. In this study, a back‐analysis technique using measured relative convergence in tunnelling is proposed. The extended Bayesian method (EBM), which combines the prior information with the field measurement data, is adopted and combined with the 3‐dimensional finite element analysis to predict ground motion. By directly using the relative convergence as observation data in the EBM, we can exclude errors that arise in the estimation of absolute displacement from measured convergence, and can evaluate the geotechnical parameters with sufficient reliability. The proposed back‐analysis technique is applied and validated by using the measured data from two tunnel sites in Korea. Copyright © 2005 John Wiley & Sons, Ltd. 相似文献