Estimation of uniaxial compressive strength of pyroclastic rocks (Cappadocia, Turkey) by gene expression programming

被引:23
作者
Ince, Ismail [1 ,2 ]
Bozdag, Ali [1 ,2 ]
Fener, Mustafa [3 ]
Kahraman, Sair [4 ]
机构
[1] Konya Tech Univ, Dept Geol Engn, TR-42250 Konya, Turkey
[2] Selcuk Univ, Dept Geol Engn, TR-42250 Konya, Turkey
[3] Ankara Univ, Dept Geol Engn, TR-06100 Ankara, Turkey
[4] Hacettepe Univ, Dept Min Engn, TR-06800 Ankara, Turkey
关键词
Uniaxial compressive strength (UCS); Pyroclastic rocks; Gene expression programming (GEP); Multiple linear regression (MLR); Construction materials; POINT-LOAD STRENGTH; OPEN-PIT MINE; TENSILE-STRENGTH; NEURAL-NETWORKS; PREDICTION; FUZZY; FLOW; DETERIORATION; PARAMETERS; CRITERION;
D O I
10.1007/s12517-019-4953-4
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
Compressive strength of rocks is an important factor in structural design in rock engineering. Compressive strength can be determined in the laboratory by means of the uniaxial compressive strength (UCS) test, or it can be estimated indirectly by simple experiments such as point load strength (PLT) test and Schmidt hammer rebound test. Although the UCS test method is time-consuming and expensive, it is simple when compared to other methods. Therefore, many studies have been performed to estimate UCS values of rocks. Studies indicated that correlation coefficient of rock groups is low unless they are classified as metamorphic, sedimentary, or volcanic. Pyroclastic rocks are widely used as construction materials because of the fact that they crop out over extensive areas in the world. To estimate the UCS values of pyroclastic rocks in Central and Western Anatolia region, Turkey, multiple linear regression (MLR) analysis and gene expression programming (GEP) were employed and during the analysis, and PLT, rho(d), rho(s), and n were used as the independent variables. Based on the analysis results, it was detected that the GEP methods gave better results than MLR method. Additionally, the correlation coefficient (R-2) values of training and sets of validation of the GEP-I model are 0.8859 and 0.9325, respectively, and this model, thereby, is detected the best of generation individuals for prediction of the UCS.
引用
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页数:13
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