LAUSR

Abstract The dynamic properties of Municipal Solid Waste (MSW) are site-specific and need to be evaluated separately in different regions. The laboratory-based evaluation of MSW has difficulties such as an unpleasant aroma or degradability of MSW, making the testing procedure unfavorable. Moreover, these evaluations are time- and cost-intensive, which may also require trained personnel to conduct the tests. To address this concern, alternatively, the shear modulus of MSW can be estimated through some predictive models. In this study, the shear modulus was evaluated using 153 cyclic triaxial tests. For this purpose, the effects of various factors, including the shear strain (ShS), age of the MSW (Age), percentage of plastic (POP), confining pressure (CP), unit weight (UW), and loading frequency (F) on the shear modulus of MSW were evaluated. The data obtained through laboratory experiments was then employed to model the dynamic response of MSW using four different machine learning techniques including Artificial Neural Networks (ANN), Multivariate Adaptive Regression Splines (MARS), Multi-Gene Genetic Programming (MGGP), and M5 model Tree (M5Tree). A comparison of the performance of developed models indicated that the ANN model outperformed the other models. More specifically, for ANN, MARS, MGGP, and M5Tree models, the corresponding values of R-squared equal to 0.9897, 0.9640, 0.9617, and 0.8482 for the training dataset, while the values for the testing dataset for ANN, MARS, MGGP, and M5Tree are 0.9812, 0.9551, 0.9574, and 0.8745. Furthermore, although the developed models using MARS and MGGP techniques resulted in more errors compared to the ANN technique, they were found to produce reliable predictions. To further compare the performance and efficiency of the developed models and study the effects of each input variable on the output variable (i.e., shear modulus), model validity, parametric study, and sensitivity analysis were performed.