Evaluation of shear strength parameters of granulated waste rubber using artificial neural networks and group method of data handling

Utilizing rubber shreds in civil engineering industry such as geotechnical structures can accelerate generated waste tire recycling process in an economical and environmentally friendly manner. However, understanding the rubber grains strength parameters is required for engineering designs and can be acquired through experimental tests. In this study, small and large direct shear test was implemented to specify shear strength parameters of five rubber grains group which are different in gradation and size. Moreover, artificial neural networks (ANN) are developed based on the test results and optimized networks which best captured the shear stress (τ), and vertical strain (εv) behavior of rubbers, are introduced. Additionally, a prediction model using the combinatorial algorithm in group method of data handling (GMDH) is proposed for the shear strength and vertical strain in the arrangement of closed-form equations. The performance and accuracies of the proposed models were checked using correlation coefficient (R) between the experimental and predicted data and the existing mean square error (MSE) was evaluated. R-values of the modeled τ and εv are equal to 0.9977 and 0.9994 for ANN, and 0.9862 and 0.9942 for GMDH models, respectively. The GMDH proposed models are presented as comparatively simple explicit mathematical equations for further applications.