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Blast-induced ground vibration prediction in granite quarries:An application of gene expression programming,ANFIS,and sine cosine algorithm optimized ANN

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International Journal of Mining Science and Technology 2021年第2期31卷 265-277页

作者： Abiodun Ismail Lawal Sangki Kwon olaide sakiru hammed Musa Adebayo Idris Department of Energy Resources Engineering Inha UniversityIncheon 22212Republic of Korea Department of Mining Engineering Federal University of TechnologyAkureNigeria Department of Physics Federal UniversityOye-EkitiNigeria Division of Mining and Geotechnical Engineering Department of CivilEnvironmental and Natural Resources EngineeringLuleåUniversity of Technology97187 LuleåSweden

Blasting of rocks has intrinsic environmental impacts such as ground vibration,which can interfere with the safety of lives and ***,accurate prediction of the environmental impacts of blasting is imperative as the empirical models are not accurate as evident in the ***,there is need to consider some robust predictive models for accurate prediction *** 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 *** 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 *** performance of the proposed models was examined using the coefficient of determination(R2)and error *** 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 *** proposed models are compared with an empirical model and are found to outperform the empirical model.

关键词： Artificial intelligence Blasting Rock density Comminution Environmental impacts Sensitivity analysis

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