典型文献
Prediction of mode Ⅰ fracture toughness of rock using linear multiple regression and gene expression programming
文献摘要：
Prediction of mode Ⅰ fracture toughness(Kic)of rock is of significant importance in rock engineering analyses.In this study,linear multiple regression(LMR)and gene expression programming(GEP)methods were used to provide a reliable relationship to determine mode Ⅰ fracture toughness of rock.The presented model was developed based on 60 datasets taken from the previous literature.To predict fracture parameters,three mechanical parameters of rock mass including uniaxial compressive strength(UCS),Brazilian tensile strength(BTS),and elastic modulus(E)have been selected as the input param-eters.A cluster of data was collected and divided into two random groups of training and testing datasets.Then,different statistical linear and artificial intelligence based nonlinear analyses were conducted on the training data to provide a reliable prediction model of KIC.These two predictive methods were then evaluated based on the testing data.To evaluate the efficiency of the proposed models for predicting the mode Ⅰ fracture toughness of rock,various statistical indices including coefficient of determination(R2),root mean square error(RMSE),and mean absolute error(MAE)were utilized herein.In the case of testing datasets,the values of R2,RMSE,and MAE for the GEP model were 0.87,0.188,and 0.156,respectively,while they were 0.74,0.473,and 0.223,respectively,for the LMR model.The results indi-cated that the selected GEP model delivered superior performance with a higher R2 value and lower errors.
文献关键词：
中图分类号：
作者姓名：
Bijan Afrasiabian;Mosleh Eftekhari
作者机构：
Department of Mining Engineering,Science and Research Branch,Islamic Azad University,Tehran,Iran;Department of Mining Engineering,Faculty of Engineering,Tarbiat Modares University,Tehran,Iran
文献出处：
引用格式：
[1]Bijan Afrasiabian;Mosleh Eftekhari-.Prediction of mode Ⅰ fracture toughness of rock using linear multiple regression and gene expression programming)[J].岩石力学与岩土工程学报（英文版）,2022(05):1421-1432
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AB值：
0.523423
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