Prediction of soil compaction under pneumatic tires a using fuzzy logic approach

Artificial intelligence systems are widely accepted as a technology offering an alternative way to tackle complex and ill-defined problems. They can learn from examples, are fault tolerant in the sense that they are able to handle noisy and incomplete data, are able to deal with non-linear problems, and once trained can perform prediction and generalization at high speed. Compared with traditional approaches, fuzzy logic is more efficient in linking the multiple inputs to a single output in a non-linear domain. The purpose of this study was to investigate the relationship between tire working parameters and soil compaction characteristics, and to illustrate how Fuzzy expert system might play an important role in prediction of soil. All experimental values were collected from soil bin. The trials were conducted in different tire types, vertical loads, inflation pressures and forvard velocities. In this paper, a sophisticated intelligent model, based on Mamdani approach fuzzy modeling principles, was developed to predict the changes in penetration resistance, final pressure and bulk density of soil due to wheel traffic. The verification of the proposed model is achieved via various numerical error criteria. For all parameters, the relative error of predicted values was found to be less than the acceptable limits (10%).