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CORRELATION OF UNIAXIAL COMPRESSIVE STRENGTH AND ELASTIC MODULUS BASED ON PARAMETER PHYSICAL PROPERTIES AND POINT LOAD OF LIMESTONES IN GUNUNG KIDUL DISTRICT: KORELASI NILAI KUAT TEKAN UNIAKSIAL DAN MODULUS ELASTIC BERDASARKAN PARAMETER SIFAT FISIK DAN POINT LOAD BATU GAMPING DI KABUPATEN GUNUNG KIDUL Albertus Pontus; Revia Oktaviani; Tommy Trides
INTAN Jurnal Penelitian Tambang Vol. 6 No. 1 (2023): INTAN Jurnal Penelitian Tambang
Publisher : Jurusan Teknik Pertambangan Program Studi S1 Teknik Pertambangan Fakultas Teknik Pertambangan dan Perminyakan Universitas Papua

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56139/intan.v6i1.164

Abstract

In mining design, it is necessary to know in advance the characteristics of physical and mechanical properties, especially in the case of limestone. Laboratory tests need to be carried out to determine the physical and mechanical characteristics of limestone. Two important parameters that are often used for geotechnical analysis are the uniaxial compressive strength (UCS) and elastic modulus (E) values. Uniaxial compressive strength and elastic modulus values can be interpreted indirectly by using statistical relationships, which are simple regression and multiple regression, to interpret the correlation between the parameters of specific gravity, porosity, and point load index (PLI). Based on the results of statistical analysis, the strongest correlation is found between the point load index (PLI) and UCS, where the correlation value is 0.85. The correlation between the point load index (PLI) and the elastic modulus also has a strong relationship, with a correlation value of 0.74. In the multiple regression analysis, with independent variables (density, porosity, and PLI) and dependent variables (UCS and elastic modulus), the average error for the uniaxial compressive strength (UCS) value is 1.7811 (multiple regression) and 1.5542 (multiple non-linear regression) while the average error for elastic modulus (E) is 0.2460 (multiple regression) and 0.1841 (multiple non-linear regression).