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您的检索式：作者名="Musa Adebayo Idris"
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| 1 | Blast-induced ground vibration prediction in granite quarries:An application of gene expression programming,ANFIS,and sine cosine algorithm optimized ANN显示文摘Blasting of rocks has intrinsic environmental impacts such as ground vibration,which can interfere with the safety of lives and property.Hence,accurate prediction of the environmental impacts of blasting is imperative as the empirical models are not accurate as evident in the literature.Therefore,there is need to consider some robust predictive models for accurate prediction results.Gene expression programming(GEP),adaptive neuro-fuzzy inference system(ANFIS),and sine cosine algorithm optimized artificial neural network(SCA-ANN)models are proposed for predicting the blast-initiated ground vibration in five granite quarries.The input parameters into the models are the distance from the point of blasting to the point of measurement(D),the weight of charge per delay(W),rock density(q),and the Schmidt rebound hardness(SRH)value while peak particle velocity(PPV)is the targeted output.100 datasets were used in developing the proposed models.The performance of the proposed models was examined using the coefficient of determination(R2)and error analysis.The R2 values obtained for the GEP,ANFIS,and SCA-ANN models are 0.989,0.997,and 0.999,respectively,while their errors are close to zero.The proposed models are compared with an empirical model and are found to outperform the empirical model. | Abiodun Ismail Lawal Sangki Kwon Olaide Sakiru Hammed Musa Adebayo Idris | 2021 | International Journal of Mining Science and Technology2021，31，2： | 3 |
| 2 | 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 |
| 3 | 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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