Use of neural networks for the prediction of the CBR value of some Aegean sands. Issue 5 (July 2016)

• Record Type: Journal Article
• Authors: Erzin, Yusuf; Turkoz, D.
• Is Part Of: Neural computing & applications. Volume 27:Issue 5(2016)
• Page Start: 1415
• Page End: 1426
• Publication Date: 2016-07
• Subjects: Aegean sands -- Artificial neural networks -- California bearing ratio -- Experimental investigations; Neural networks (Computer science) -- Periodicals; Neural circuitry -- Periodicals; Artificial intelligence -- Periodicals; Neural Networks (Computer) -- Periodicals; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux nerveux -- Périodiques; Intelligence artificielle -- Périodiques; 006.32
• DOI: 10.1007/s00521-015-1943-7
• Languages: English
• ISSNs: 0941-0643

Abstract

Abstract This study deals with the development of an artificial neural network (ANN) and a multiple regression (MR) model that can be employed for estimating the California bearing ratio (CBR) value of some Aegean sands. To achieve this, the results of CBR tests performed on the compacted specimens of nine different Aegean sands with varying soil properties were used in the development of the ANN and MR models. The results of the ANN and MR models were compared with those obtained from the experiments. It is found that the CBR values predicted from the ANN model matched the experimental values much better than the MR model. Moreover, several performance indices, such as coefficient of determination, root-mean-square error, mean absolute error, and variance, were used to evaluate the prediction performance of the ANN and MR models. The ANN model has shown higher prediction performance than the MR model based on the performance indices, which demonstrates the usefulness and efficiency of the ANN model. Thus, the ANN model can be used to predict CBR value of the Aegean sands included in this study as an inexpensive substitute for the laboratory testing, quite easily and efficiently.