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Acta Geotechnica - Soils have a variety of mineral compositions. Although a number of thermal conductivity models have been developed for soils, few quantitatively investigated the effect of... 相似文献
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Acta Geotechnica - Artificial ground freezing (AGF) has been widely used as a temporary soil stabilization and waterproofing technique in geotechnical practices (e.g., tunnel construction). Many... 相似文献
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Acta Geotechnica - The multi-scale characteristics of particle morphology, including the overall form, local roundness and surface roughness, affect the critical state behavior of sands and should... 相似文献
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Reliability analysis of serviceability performance for an underground cavern using a non-intrusive stochastic method 总被引：1，自引：0，他引：1
Dian-Qing Li Shui-Hua Jiang Yi-Feng Chen Chuang-Bing Zhou 《Environmental Earth Sciences》2014,71(3):1169-1182
This paper proposes a non-intrusive stochastic analysis procedure for reliability analysis of the serviceability performance of an underground cavern with an implicit limit state function. This procedure is formulated on the basis of the stochastic response surface method (SRSM) and the deterministic finite element method. First, the SRSM is briefly introduced and implemented through a MATLAB code. Then, the software SIGMA/W is used to perform a deterministic finite element analysis. Next, a link between the MATLAB code and SIGMA/W is developed to automatically pass exchange data between the two platforms. Finally, two examples are presented to illustrate the capacity and validity of the proposed procedure. In the first example, a closed-form limit state function is adopted to validate the SRSM by comparing it with the results obtained from a direct Monte Carlo simulation. In the second example, the serviceability performance of an underground cavern is analyzed to illustrate the capacity of the proposed procedure to handle a reliability problem with an implicit limit state function. The proposed procedure does not require the user to modify the existing deterministic finite element code. The deterministic finite element analysis and the probabilistic analysis are decoupled. This is a major practical advantage because realistic probabilistic analyses are made possible. The SRSM can produce sufficiently accurate reliability results. Furthermore, the method is much more efficient than the direct Monte Carlo simulation. Sensitivity analyses show the effect of the variability of input random variables and the correlation between them on: (1) the probability density functions, (2) the first four order statistical moments, and (3) the probability of failure, which is investigated and discussed. 相似文献
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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
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Enhancement of random finite element method in reliability analysis and risk assessment of soil slopes using Subset Simulation 总被引：4，自引：2，他引：2
Dian-Qing?Li Te?Xiao Zi-Jun?CaoEmail author Chuang-Bing?Zhou Li-Min?Zhang 《Landslides》2016,13(2):293-303
Random finite element method (RFEM) provides a rigorous tool to incorporate spatial variability of soil properties into reliability analysis and risk assessment of slope stability. However, it suffers from a common criticism of requiring extensive computational efforts and a lack of efficiency, particularly at small probability levels (e.g., slope failure probability P f ?<?0.001). To address this problem, this study integrates RFEM with an advanced Monte Carlo Simulation (MCS) method called “Subset Simulation (SS)” to develop an efficient RFEM (i.e., SS-based RFEM) for reliability analysis and risk assessment of soil slopes. The proposed SS-based RFEM expresses the overall risk of slope failure as a weighed aggregation of slope failure risk at different probability levels and quantifies the relative contributions of slope failure risk at different probability levels to the overall risk of slope failure. Equations are derived for integrating SS with RFEM to evaluate the probability (P f ) and risk (R) of slope failure. These equations are illustrated using a soil slope example. It is shown that the P f and R are evaluated properly using the proposed approach. Compared with the original RFEM with direct MCS, the SS-based RFEM improves, significantly, the computational efficiency of evaluating P f and R. This enhances the applications of RFEM in the reliability analysis and risk assessment of slope stability. With the aid of improved computational efficiency, a sensitivity study is also performed to explore effects of vertical spatial variability of soil properties on R. It is found that the vertical spatial variability affects the slope failure risk significantly. 相似文献
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Effect of mesoscale internal structure on effective thermal conductivity of anisotropic geomaterials
Acta Geotechnica - Geomaterials tend to be stratified due to the geological process, and their properties are often anisotropic. In this study, the quartet structure generation set (QSGS) method is... 相似文献
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Acta Geotechnica - In situ monitoring provides valuable information to update the predictions of the embankment settlement on soft soils. Observational data obtained at different monitoring moments... 相似文献
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Acta Geotechnica - Earth slopes usually exhibit a rotational failure mode when subjected to seismic shaking. In current seismic rotational slope displacement analysis, the slopes are usually... 相似文献
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