Estimation of California bearing ratio by using soft computing systems
This study presents the application of different methods (simple-multiple analysis and artificial neural networks) for the estimation of the California bearing ratio (CBR) from sieve analysis, Atterberg limits, maximum dry unit weight and optimum moisture content of the soils. The resistance of granular soils, which are in the superstructure foundation and subgrade layers are usually tested by CBR (California bearing ratio), which is an old and still extensively used experiment. The data were collected from the public highways of Turkey's different regions. Regression analysis and artificial neural network estimation indicated strong correlations (R-2 = 0.80-0.95) between the sieve analysis, Atterberg limits, maximum dry unit weight (MOD) and optimum moisture content (OMC). 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. (c) 2010 Elsevier Ltd. All rights reserved.
artificial neural networks california bearing ratio (cbr) sieve analysis atterberg limits maximum dry unit weight optimum moisture content correlation