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Estimation of soil compaction parameters by using statistical analyses and artificial neural networks

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Environmental Geology

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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Acknowledgments

The author would like to thank Dr. Cafer Kayadelen from Kahramanmaraş Sütçü İmam University and to İlhami Göktürk from Nigde Special Provincial Administration for their assistance.

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Authors and Affiliations

  1. Department of Geology Engineering, Nigde University, 51100, Nigde, Turkey

    O. Günaydın

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  1. O. Günaydın
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Günaydın, O. Estimation of soil compaction parameters by using statistical analyses and artificial neural networks. Environ Geol 57, 203–215 (2009). https://doi.org/10.1007/s00254-008-1300-6

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  • DOI: https://doi.org/10.1007/s00254-008-1300-6

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