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THE IMPACT AGGREGATE QUALITY MATERIAL AS A LINEAR REGRESSION STUDY ON MIXTURE CONCRETE
Author(s) -
Ranti Hidayawanti
Publication year - 2020
Publication title -
international journal of geomate
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 17
eISSN - 2186-2990
pISSN - 2186-2982
DOI - 10.21660/2020.70.5611
Subject(s) - aggregate (composite) , quality (philosophy) , linear regression , econometrics , environmental science , statistics , materials science , mathematics , composite material , philosophy , epistemology
Fine aggregate and coarse aggregate are important components in making concrete for light and structure concrete. These mixture concretes are one of the elements as structural component in building construction. Generally, this aggregate is easy to find both coarse and fine aggregates beside the aggregate can be used as a component on concrete in the building which needs strong support. The objectives of this study are adding mixture aggregate with compared to designed result (theory) mixture concrete by using the method of fine modulus and linear regression. Those two methods will be found out theoretically in optimizing certain components (economic). In order to get the presentation of aggregate and meet the requirement of standard quality concrete, are used the comparison of fine and coarse aggregate at 60% 40%. The method that is used to pass the screening is fine modulus and the counting of mixture aggregate used statistic formula calculation which is regression linear method to find the percentage comparison between coarse and fine aggregate in designing of a mix that can be used in making concrete. By knowing the maximum aggregate limit, it is expected to find out the limitation aggregate of fine and coarse accurately so that the result of concrete that is used is better because the raw material is correct for the construction composition. From the test results for fine aggregate R2 = 0.9444 and coarse aggregates R2 =0.9581 with linear regression formulation is and compressive strength shows the value of R2 = 0.9627, then the strength of the relationship between variables can be expressed by the magnitude of the correlation coefficient value on the linear function.

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