Prognostication of vertical stress transmission in soil profile by adaptive neuro-fuzzy inference system based modeling approach

Prediction of vertical stress transmission in real soil profile using adaptive neuro-fuzzy inference system (ANFIS) is documented in this investigation. A soil bin facility holding a single-wheel tester was utilized to arrange controlled condition for exploration of the effects of wheel load, forward velocity, slippage and depth each at three different levels. A profile housing seven load cells was buried at different depths when data were transmitted to a data acquisitioning system for derivation of 81 data points and then to build ANFIS-based model. The Sugeno-type fuzzy rules were constituted with various membership functions in the representations. In the Sugeno-type fuzzy inference approach, the modal was developed according to the four input parameters. Performance evaluation criteria (i.e. MSE, MRE and R-2) were incorporated in the study to find the highest quality solution. It was deduced, on the basis of performance criteria, that a Guassian membership function outperformed other tested membership functions. The results could serve as a catalyst to expedite the investigations in the realm of artificial intelligence application in prediction of soil stress transmission created by wheeled vehicle trafficking. (C) 2013 Elsevier Ltd. All rights reserved.