Application of adaptive neuro-fuzzy inference system for prediction of internal stability of soils

Abstract

This paper presents an adaptive neuro-fuzzy inference system (ANFIS) for the assessment of internal stability of soils under seepage. The training of fuzzy system was performed by a hybrid method of back-propagation (BP) and least mean square algorithm, and the subtractive clustering algorithm was utilised for optimising the number of fuzzy rules. Experimental data on internal stability of soils in the literature were used to validate and evaluate the performance of the proposed ANFIS model. Further, predictions from three models (the BP model, the particle swarm optimisation-BP model and the ANFIS model) were compared with the experimental data. The results show that the ANFIS model is a feasible, efficient and accurate tool for predicting the internal stability of soils according to Wan and Fell’s criterion.

Keywords:

• internal stability
• adaptive neuro-fuzzy inference system
• particle swarm optimisation
• back propagation
• subtractive clustering algorithm

Disclosure statement

No potential conflict of interest was reported by the authors.