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


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2009-03

AUTHORS

O. Günaydın

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 (r2 = 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. More... »

PAGES

203

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00254-008-1300-6

DOI

http://dx.doi.org/10.1007/s00254-008-1300-6

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1008234664


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