Prediction of artificial soil's unconfined compression strength test using statistical analyses and artificial neural networks

Authors:
Osman Gunaydin;Ali Gokoglu;Mustafa Fener
Affiliations:
Nigde University, Department of Geological Engineering, 51200 Nigde, Turkey;Cukurova University, Vocational School of Ceyhan, 01960 Ceyhan/Adana, Turkey;Nigde University, Department of Geological Engineering, 51200 Nigde, Turkey
Venue:
Advances in Engineering Software
Year:
2010

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Abstract

Laboratory prediction of the unconfined compression strength (UCS) of cohesive soils is important to determine the shear strength properties. However, this study presents the application of different methods simple-multiple analysis and artificial neural networks for the prediction of the UCS from basic soil properties. Regression analysis and artificial neural networks prediction indicated that there exist acceptable correlations between soil properties and unconfined compression strength. Besides, artificial neural networks showed a higher performance than traditional statistical models for predicting UCS. Regression analysis and artificial neural network prediction indicated strong correlations (R^2=0.71-0.97) between basic soil properties and UCS. It has been shown that the correlation equations obtained by regression analyses are found to be reliable in practical situations.