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| Stochastic back analysis and comparison of spatially varying geotechnical mechanical parameters based on limited data#br# |
| JIANG Shuihua1,2,LIU Yuan1,ZHANG Xiaobo1,2,HUANG Jinsong1,ZHOU Chuangbing1,2#br# |
(1. School of Civil Engineering and Architecture,Nanchang University,Nanchang,Jiangxi 330031,China;2. Key Laboratory of Tailings Reservoir Engineering Safety of Jiangxi Province,Nanchang University,Nanchang,Jiangxi 330031,China)
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Abstract In-situ and laboratory test data are often quite limited,by which it is hard to determine the statistical characteristics of geomechanical parameters. Fortunately,the stochastic back analysis method provides an approach to overcome the shortcoming. In this paper,three stochastic back analysis methods(i.e.,DREAM(zs),BUS and aBUS) of geomechanical parameters accounting for the effect of spatial variation are developed,and the basic principles of the three methods are compared from the aspects of generation of random samples,convergence criterion,model evidence and estimation of posterior probability of failure. Two slope examples are investigated to further compare these three methods systematically on the convergence,computational accuracy and efficiency. Based on these,the DREAM(zs),BUS and aBUS methods are respectively recommended to give priority to tackle different stochastic back analysis problems. The results indicate that the DREAM(zs) method has good computational accuracy and efficiency only for dealing with low-dimensional problems,that the BUS method,in which the value of likelihood function multiplier has to be determined before the operation of subset simulation,is preferable to solve high-dimensional problems involving the spatial variability of mechanical parameters and intensive computations likelihood function,and that the aBUS method,which does not rely on the likelihood function multiplier and has good computational accuracy,is fairly suitable for analyzing high-dimensional problems involving the spatial variability of mechanical parameters and less computation of likelihood function,although it is time consuming to quantitatively determine whether the computations converge to the accurate results.
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