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| 1 | Influence of rock property correlation on reliability analysis of rock slope stability: From property characterization to reliability analysis显示文摘Cohesion(c) and friction angle(φ) of rock are important parameters required for reliability analysis of rock slope stability. There is correlation between c and φ which affects results of reliability analysis of rock slope stability. However, the characterization of joint probability distribution of c and φ through which their correlation can be estimated requires a large amount of rock property data, which are often not available for most rock engineering projects. As a result, the correlation between c and φ is often ignored or simply assumed during reliability studies, which may lead to bias estimation of failure probability. In probabilistic rock slope stability analysis, the influence of ignoring or simply assuming the correlation of the rock strength parameters(i.e., c and φ) on the reliability of rock slopes has not been fully investigated. In this study, a Bayesian approach is developed to characterize the correlation between c and φ, and an expanded reliability-based design(RBD) approach is developed to assess the influence of correlation between c and φ on reliability of a rock slope. The Bayesian approach characterizes the sitespecific joint probability distribution of c and φ, and quantifies the correlation between c and φ using available limited data pairs of c and φ from a rock project. The expanded RBD approach uses the joint probability distribution of c and φ obtained through the Bayesian approach as inputs, to determine the reliability of a rock slope. The approach gives insight into the propagation of the correlation between c and φ through their joint probability into the reliability analysis, and their influence on the calculated reliability of the rock slope. The approaches may be applied in practice with little additional effort from a conventional analysis. The proposed approaches are illustrated using real c and φ data pairs obtained from laboratory tests of fractured rock at Forsmark, Sweden. | Adeyemi Emman Aladejare Yu Wang | 2018 | Geoscience Frontiers2018，9，6： | 5 |
| 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 | 基于贝叶斯方法的模型选择以及岩石性质概率表征显示文摘岩土工程勘察中,工程师通常从已知岩土性质间接估计岩土的其他性质.例如,工程师会根据岩样的标准点荷载指标(Is(50))间接估计岩石的单轴抗压强度(uniaxial compressive strength,UCS).间接估计时会应用Is(50)和UCS之间的回归模型,然而,现存文献中这种回归模型众多,使得工程师在具体工程应用中难以选择.基于此,阐述了如何通过贝叶斯方法结合Is(50)数据和现场先验信息选择模型,以及选定模型后如何对UCS进行概率表征.所述方法通过工程实例进行了说明.结果显示,所述方法可以单独依据Is(50)数据以及现场先验信息选出合适的模型.且依据所选模型进行UCS的概率表征结果和工程现场情况相吻合. | 赵腾远 ALADEJARE Adeyemi Emman 王宇 | 2016 | 武汉大学学报(工学版)2016，49，5： | 3 |
| 4 | A comparative study of geometric and geostatistical methods for qualitative reserve estimation of limestone deposit显示文摘Mining projects especially relating to limestone deposits require an accurate knowledge of tonnage and grade,for both short and long-term planning.This is often difficult to establish as detailed exploration operations,which are required to get the accurate description of the deposit,are costly and time consuming.Geologists and mining engineers usually make use of geometric and geostatistical methods,for estimating the tonnage and grade of ore reserves.However,explicit assessments into the differences between these methods have not been reported in literature.To bridge this research gap,a comparative study is carried out to compare the qualitative reserve of Oyo-Iwa limestone deposit located in Nigeria,using geometric and geostatistical methods.The geometric method computes the reserve of the limestone deposit as 74,536,820 t(mean calcite,CaO grade=52.15)and 99,674,793 t(mean calcite,CaO grade=52.32),for the Northern and Southern zones of the deposit,respectively.On the other hand,the geostatistical method calculates the reserve as 81,626,729.65 t(mean calcite,CaO grade=53.36)and 100,098,697.46 t(mean calcite,CaO grade=52.96),for the two zones,respectively.The small relative difference in tonnage estimation between the two methods(i.e.,9.51%and 0.43%),proves that the geometric method is effective for tonnage estimation.In contrast,the relative difference in grade estimation between the two methods(i.e.,2.32%and 1.26%)is not negligible,and could be crucial in maintaining the profitability of the project.The geostatistical method is,therefore,more suitable,reliable and preferable for grade estimation,since it involves the use of spatial modelling and cross-validated interpolation.In addition,the geostatistical method is used to produce quality maps and three-dimensional(3-D)perspective view of the limestone deposit.The quality maps and 3-D view of the limestone deposit reveal the variability of the limestone grade within the deposit,and it is useful for operational management of the limestone raw materials.The qualitative mapping of the limestone deposit is key to effective production scheduling and accurate projection of raw materials for cement production. | Thomas Busuyi Afeni Victor Oluwatosin Akeju Adeyemi Emman Aladejare | 2021 | Geoscience Frontiers2021，12，1： | 1 |
| 5 | Predictions of elemental composition of coal and biomass from their proximate analyses using ANFIS, ANN and MLR显示文摘The elemental composition of coal and biomass provides significant parameters used in the design of almost all energy conversion systems and projects.The laboratory tests to determine the elemental composition of coal and biomass is time-consuming and costly.However,limited research has suggested that there is a correlation between parameters obtained from elemental and proximate analyses of these materials.In this study,some predictive models of the elemental composition of coal and biomass using soft computing and regression analyses have been developed.Thirty-one samples including parameters of elemental and proximate analyses were used during the analyses to develop multiple prediction models.Dependent variables for multiple prediction models were selected as carbon,hydrogen,and oxygen.Using volatile matter,fixed carbon,moisture and ash contents as independent variables,three different prediction models were developed for each dependent parameter using ANFIS,ANN,and MLR.In addition,a routine for selecting the best predictive model was suggested in the study.The reliability of the established models was tested by using various prediction performance indices and the models were found to be satisfactory.Therefore,the developed models can be used to determine the elemental composition of coal and biomass for practical purposes. | Abiodun Ismail Lawal Adeyemi Emman Aladejare Moshood Onifade Samson Bada Musa Adebayo Idris | 2021 | International Journal of Coal Science & Technology2021，8，1： | 1 |
| 6 | 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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