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| 1 | Probabilistic analysis of tunnel displacements based on correlative recognition of rock mass parameters显示文摘Displacement is vital in the evaluations of tunnel excavation processes,as well as in determining the postexcavation stability of surrounding rock masses.The prediction of tunnel displacement is a complex problem because of the uncertainties of rock mass properties.Meanwhile,the variation and the correlation relationship of geotechnical material properties have been gradually recognized by researchers in recent years.In this paper,a novel probabilistic method is proposed to estimate the uncertainties of rock mass properties and tunnel displacement,which integrated multivariate distribution function and a relevance vector machine(RVM).The multivariate distribution function is used to establish the probability model of related random variables.RVM is coupled with the numerical simulation methods to construct the nonlinear relationship between tunnel displacements and rock mass parameters,which avoided a large number of numerical simulations.Also,the residual rock mass parameters are taken into account to reflect the brittleness of deeply buried rock mass.Then,based on the proposed method,the uncertainty of displacement in a deep tunnel of CJPL-II laboratory are analyzed and compared with the in-situ measurements.It is found that the predicted tunnel displacements by the RVM model closely match with the measured ones.The correlations of parameters have significant impacts on the uncertainty results.The uncertainty of tunnel displacement decreases while the reliability of the tunnel increases with the increases of the negative correlations among rock mass parameters.When compared to the deterministic method,the proposed approach is more rational and scientific,and also conformed to rock engineering practices. | Minzong Zheng Shaojun Li Hongbo Zhao Xiang Huang Shili Qiu | 2021 | Geoscience Frontiers2021，12，4： | 6 |
| 2 | Evaluation of empirical estimation of uniaxial compressive strength of rock using measurements from index and physical tests显示文摘The uniaxial compressive strength(UCS) of rock is an important parameter required for design and analysis of rock structures,and rock mass classification.Uniaxial compression test is the direct method to obtain the UCS values.However,these tests are generally tedious,time-consuming,expensive,and sometimes impossible to perform due to difficult rock conditions.Therefore,several empirical equations have been developed to estimate the UCS from results of index and physical tests of rock.Nevertheless,numerous empirical models available in the literature often make it difficult for mining engineers to decide which empirical equation provides the most reliable estimate of UCS.This study evaluates estimation of UCS of rocks from several empirical equations.The study uses data of point load strength(Is(50)),Schmidt rebound hardness(SRH),block punch index(BPI),effective porosity(n) and density(ρ)as inputs to empirically estimate the UCS.The estimated UCS values from empirical equations are compared with experimentally obtained or measured UCS values,using statistical analyses.It shows that the reliability of UCS estimated from empirical equations depends on the quality of data used to develop the equations,type of input data used in the equations,and the quality of input data from index or physical tests.The results show that the point load strength(Is(50)) is the most reliable index for estimating UCS among the five types of tests evaluated.Because of type-specific nature of rock,restricting the use of empirical equations to the similar rock types for which they are developed is one of the measures to ensure satisfactory prediction performance of empirical equations. | Adeyemi Emman Aladejare | 2020 | Journal of Rock Mechanics and Geotechnical Engineering2020，12，2： | 4 |
| 3 | Reliability analysis of geotechnical infrastructures: Introduction显示文摘In the design of geotechnical infrastructure,engineers have to deal with naturally occurring soils and rocks which are subjected to spatial variability as well as other uncertainties such as errors in measurement and in modeling methods.Reliability assessment which provides a systematic approach for quantifying the risk of failure has been shown to be a promising tool for solving these challenging geotechnical engineering problems.The method provides a more consistent measure of the level of safety or“structural reliability”through the evaluation of a reliability index and the associated“failure”probability,and is a method that satisfies the need to clearly convey safety issues to the public and regulatory authorities.Various methods for calculating the reliability of geotechnical infrastructures with regard to the assessment of the ultimate and serviceability limit states have been proposed by many researchers and these approaches include:the direct Monte Carlo Simulation,Bayesian and other sampling techniques,the first-order reliability method and the second-order reliability method,the random field method,the response surface method and other surrogate models with the related probabilistic procedures.In this special issue of Geoscience Frontiers,we assemble eleven invited papers which provide insights on the latest developments and challenges in applying probabilistic and reliability methods to geotechnical infrastructure design. | Wengang Zhang Anthony Teck Chee Goh | 2018 | Geoscience Frontiers2018，9，6： | 2 |
| 4 | Similarity quantification of soil parametric data and sites using confidence ellipses显示文摘This paper presents a confidence ellipse-based method to evaluate the similarity of soil parametric data using the database from the site investigation reports.Then,the obtained similarity assessment results of parametric data are used to further estimate the site similarity via two proposed strategies,namely the mean and weighted mean approaches.The former referred to the average of parametric data similarity degrees,while the latter was the weighted average,and the weight was calculated using the coefficient of variation(COV)of each parameter.For illustration,the liquidity index(LI)dataset was firstly used to explore the performance of the presented method in the evaluation of parametric data similarity.Subsequently,the site similarity was assessed and the effects of numbers and weights of selected parameters for study were systematically studied.Lastly,the transformation models about the relationships between Cc and x as well as between Cc and e0 were constructed to illustrate the application of the similarity analysis in reduction of transformation uncertainty.Results show that the greatest site similarity degree is at about 0.76 in this study,and the maximum decrease of transformation uncertainty can reach up to 18%and 25.5%as union parametric data similarity degree increases.Moreover,the site similarity degree represents the whole similarity between two different sites,and the presented union parameter similarity degree maintains a good agreement with transformation uncertainty. | Liang Han Lin Wang Xuanming Ding Haijia Wen Xingzhong Yuan Wengang Zhang | 2022 | Geoscience Frontiers2022，13，1： | 1 |
| 5 | Performance analysis of empirical models for predicting rock mass deformation modulus using regression and Bayesian methods显示文摘Deformation modulus of rock mass is one of the input parameters to most rock engineering designs and constructions.The field tests for determination of deformation modulus are cumbersome,expensive and time-consuming.This has prompted the development of various regression equations to estimate deformation modulus from results of rock mass classifications,with rock mass rating(RMR)being one of the frequently used classifications.The regression equations are of different types ranging from linear to nonlinear functions like power and exponential.Bayesian method has recently been developed to incorporate regression equations into a Bayesian framework to provide better estimates of geotechnical properties.The question of whether Bayesian method improves the estimation of geotechnical properties in all circumstances remains open.Therefore,a comparative study was conducted to assess the performances of regression and Bayesian methods when they are used to characterize deformation modulus from the same set of RMR data obtained from two project sites.The study also investigated the performance of different types of regression equations in estimation of the deformation modulus.Statistics,probability distributions and prediction indicators were used to assess the performances of regression and Bayesian methods and different types of regression equations.It was found that power and exponential types of regression equations provide a better estimate than linear regression equations.In addition,it was discovered that the ability of the Bayesian method to provide better estimates of deformation modulus than regression method depends on the quality and quantity of input data as well as the type of the regression equation. | Adeyemi Emman Aladejare Musa Adebayo Idris | 2020 | Journal of Rock Mechanics and Geotechnical Engineering2020，12，6： | 0 |
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