Estimation of soil compaction parameters by using statistical analyses and artificial neural networks |
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Authors: | Email author" target="_blank">O?Günayd?nEmail author |
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Institution: | (1) Department of Geology Engineering, Nigde University, 51100 Nigde, Turkey |
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Abstract: | This study presents the application of different methods (simple–multiple analysis and artificial neural networks) for the
estimation of the compaction parameters (maximum dry unit weight and optimum moisture content) from classification properties
of the soils. Compaction parameters can only be defined experimentally by Proctor tests. The data collected from the dams
in some areas of Nigde (Turkey) were used for the estimation of soil compaction parameters. Regression analysis and artificial
neural network estimation indicated strong correlations (r
2 = 0.70–0.95) between the compaction parameters and soil classification properties. It has been shown that the correlation
equations obtained as a result of regression analyses are in satisfactory agreement with the test results. It is recommended
that the proposed correlations will be useful for a preliminary design of a project where there is a financial limitation
and limited time. |
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Keywords: | Compaction parameters Atterberg limits Soil properties Artificial neural networks Correlation |
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