Soft computing techniques for the prediction of non-convergent behaviour in iron tailings considering compositional attributes

Prediction of non-convergent characteristics in a non-conforming geomaterials is very essential due to unpredictable nature of their behaviour. One of the important factors contributing to these materials mechanical behaviour is compositions (chemical and mineralogical). This paper presents soft computing techniques for the prediction of non-convergent behaviour in iron tailings considering compositional attributes. This was achieved by adopting artificial neural network (ANN) and adaptive neuro-fuzzy inference systems (ANFIS) to predict non-convergent behaviour in iron tailings. Both ANN and ANFIS successfully predicted non-convergent behaviour using chemical composition (silicon oxide SiO 2 , aluminum oxide Al 2 O 3 , iron oxide Fe 2 O 3 ), mineralogical composition (quartz Qz, hematite He, kaolinite Ke) and combined compositions (SiO 2 , Al 2 O 3 , Fe 2 O 3 , Qz, He, Ke). Overall, ANFIS proved to be the most appropriate predictive models for transitional behaviour using chemical composition, mineralogical composition and combined compositions with an average performance indices R 2 , a20, a10, MAE, RMSE and MAPE of 0.94, 1.00, 0.95, 0.013, 0.029 and 2.96 respectively. Influence of sample preparations is observed in ANN model using chemical composition, mineralogical composition and combined compositions. However, sample preparations do not have effect in ANFIS model using chemical composition, mineralogical composition and combined compositions. This further confirms that ANFIS will predict non-convergent behaviour better than the ANN. Sensitivity analysis showed that hematite exert strongest control over the transitional behaviour followed by the quartz, iron oxide, silicon oxide, kaolinite and aluminum oxide.