Determination of Shear Strength Parameters of Municipal Solid Waste from Its Physical Properties

In this research, artificial neural networks (ANNs) were used to present models for predicting the shear strength parameters of the municipal solid waste (MSW) obtained from the triaxial tests results from the literature. Two kinds of neural networks were used in this paper, including radial basis function (RBF) and multilayer perceptron (MLP). Physical properties of MSW, including the fiber content, moisture content, dry unit weight and axial strain, were used as the input data and the drained MSW shear strength parameters; that is, cohesion and friction angle were considered as the output data. In this paper, 80 and 90% of the total set of data were used for training of the networks and then they were tested for the total set of data (100% of the data) to obtain the best neural network model and to find out the effect of learning for prediction of both sets of data (learning and testing set of data). The results of the correlation coefficient parameter lead us to the fact that the networks with similar geometry, but more training data, predict more accurately. This value for the MLP with 80% training data is the minimum and equal to 0.95 and 0.86 for the cohesion and friction angle, respectively. It is maximum for RBF network with 90% training data and equal to 0.97 and 0.89 for the cohesion and friction angle, respectively. In addition, the RBF networks predict less mean relative values relative to the MLP networks. Among the different networks, mean relative error for RBF network with 90% training data is minimum and equal to 7.4 for the cohesion and 8 for the friction angle, respectively. Based on the analysis of the statistical parameters, the RBF network predicts more accurately. Results show that the ANNs are powerful tools for predicting the shear strength parameters of MSW.