Prediction of tire tractive performance by using artificial neural networks

Abstract

The purpose of this study was to investigate the relationship between travel reduction and tractive performance and to illustrate how artificial neural networks (ANNs) could play an important role in the prediction of these parameters. The experimental values were taken in a soil bin. A 1-4-6-2 artificial neural network (ANN) model with a back propagation learning algorithm was developed to predict the tractive performance of a driven tire in a clay loam soil under varying operating and soil conditions. The input parameter of the network was travel reduction. The output parameters of the network were net traction ratio and tractive efficiency. The relationships were investigated using non-linear regression analysis and ANNs. The performance of the neural network-based model was compared with the performance of a non linear regression-based model using the same observed data. It was found that the ANN model consistently gave better predictions compared to the non linear regression-based model. Based on the results of this study, ANNs appear to be a promising technique for predicting tire tractive performance.