Modeling of UCS value of stabilized pond ashes using adaptive neuro-fuzzy inference system and artificial neural network

Abstract

This paper investigates the capability of adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) modeling approach to predict the unconfined compressive strength (UCS) of stabilized pond ashes with lime alone and in combination with lime sludge. Out of 170 data set, a total of 119 data were randomly selected for training, whereas remaining 51 were used for testing the model. Four membership’s functions (MFs) such as Gaussian, generalized bell-shaped, triangular, and trapezoidal were used with ANFIS model. Statistical parameters were used to compare the performance of four MF-based ANFIS and ANN models. A comparison of results suggests that Triangular MF-based ANFIS model exhibit better predictive performance with higher CC = 0.980 and lower MSE = 3028.515 and RMSE = 55.032 than other MF-based ANFIS and ANN model. The results of single-factor analysis of variance indicate that there is an insignificant difference between measured and predicted values of UCS using different models. Further, results of sensitivity analysis depict that the curing period, lime sludge, and lime are the most important parameters which affect the performance of Triangular MF-based ANFIS in predicting the UCS of stabilized pond ashes. Thus, the Triangular MF-based ANFIS model could be a useful tool in predicting the UCS value of stabilized pond ashes because of its adequacy in handling uncertainties in the test results with accuracy.