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Estimating uniaxial compressive strength of rocks using genetic expression programming 被引量:1
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作者 Ahmet Ozbek Mehmet Unsal Aydin Dikec 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2013年第4期325-329,共5页
The aim of this paper is to estimate the uniaxial compressive strength(UCS) of rocks with different characteristics by using genetic expression programming(GEP).For this purpose,five different types of rocks inclu... The aim of this paper is to estimate the uniaxial compressive strength(UCS) of rocks with different characteristics by using genetic expression programming(GEP).For this purpose,five different types of rocks including basalt and ignimbrite(black,yellow,gray,brown) were prepared.Values of unit weight,water absorption by weight,effective porosity and UCS of rocks were determined experimentally.By using these experimental data,five different GEP models were developed for estimating the values of UCS for different rock types.Good agreement between experimental data and predicted results is obtained. 展开更多
关键词 Uniaxial compressive strength(UCS) genetic expression programming(GEP) Rock masses
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Comparative evaluation of different statistical tools for the prediction of uniaxial compressive strength of rocks 被引量:10
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作者 Ahmet Teymen Engin Cemal Mengüç 《International Journal of Mining Science and Technology》 SCIE EI CAS CSCD 2020年第6期785-797,共13页
In this study,uniaxial compressive strength(UCS),unit weight(UW),Brazilian tensile strength(BTS),Schmidt hardness(SHH),Shore hardness(SSH),point load index(Is50)and P-wave velocity(Vp)properties were determined.To pre... In this study,uniaxial compressive strength(UCS),unit weight(UW),Brazilian tensile strength(BTS),Schmidt hardness(SHH),Shore hardness(SSH),point load index(Is50)and P-wave velocity(Vp)properties were determined.To predict the UCS,simple regression(SRA),multiple regression(MRA),artificial neural network(ANN),adaptive neuro-fuzzy inference system(ANFIS)and genetic expression programming(GEP)have been utilized.The obtained UCS values were compared with the actual UCS values with the help of various graphs.Datasets were modeled using different methods and compared with each other.In the study where the performance indice PIat was used to determine the best performing method,MRA method is the most successful method with a small difference.It is concluded that the mean PIat equal to 2.46 for testing dataset suggests the superiority of the MRA,while these values are 2.44,2.33,and 2.22 for GEP,ANFIS,and ANN techniques,respectively.The results pointed out that the MRA can be used for predicting UCS of rocks with higher capacity in comparison with others.According to the performance index assessment,the weakest model among the nine model is P7,while the most successful models are P2,P9,and P8,respectively. 展开更多
关键词 Uniaxial compressive strength Adaptive neuro-fuzzy inference system Multiple regression Artificial neural network genetic expression programming
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