Estimation of Optimal Cone Index by Using Neural Networks

Rolling resistance is considered as a significant criterion in vehicles energy consumption. In off-road vehicles, soil and tire interaction encompasses more parameters that lead to the thorny problem in its analysis. The typical experimental models are useful remedies in the estimation perspective, but they can not be extended to all conditions. Neural networks as efficient tools can simplify the examination procedure. This research was aimed to raise the Wismer-Luth model accuracy to rely on cone index as soil condition representative by utilizing three neural networks. Three types of networks were investigated, comprising multi-layer perceptron (MLP), radial basis function (RBF) and generalized regression neural network (GRNN). Various feature vectors consisting of vertical load parameters, velocity, traffic, and cone index values were evaluated to estimate the optimal value of the cone index. GRNN had the best performance with the least error rate in comparison with RBF and MLP. Based on the results of feature vector analysis, employing either forward velocity or multi-pass factors had a negligible effect on model improvement, but by incorporating all parameters (velocity, load, and number of passes), the accuracy of the model increased significantly.

Automated summary

The research aimed to improve the accuracy of the Wismer-Luth model in predicting cone index by utilizing three neural networks, with the generalized regression neural network showing the best performance. It was also found that using forward velocity or multi-pass factors had minimal impact on model improvement, but incorporating all parameters (velocity, load, and number of passes) significantly increased model accuracy.