Mechanical Characterization of Coarse-Grained Waste Rocks Using Large-Scale Triaxial Tests and Neuroevolution of Augmenting Topologies

Mining operations produce large quantities of waste rocks, which are usually disposed of in waste rock piles, but can also be valorized in mine haul roads. The engineering performance of these haul roads significantly depends on the mechanical characteristics of the materials used for the construction. However, available experimental studies on coarse-grained waste rocks are relatively limited, mainly because of their large grain size and the scarcity of adapted testing equipment. In this study, a series of repeated load and monotonic triaxial tests (specimens 300 mm in diameter and 600 mm in height) were carried out to evaluate the resilient modulus, permanent deformation, and shear strength of coarse-grained waste rocks (up to 60 mm in diameter) with different gradations. Results showed that an increasing in maximum particle size and compaction effort resulted in a larger resilient modulus and shear strength and smaller permanent deformation. The optimal gravel-to-sand ratio to maximize resilient modulus and shear strength was around 5. Permanent strain was relatively constant when the gravel-to-sand ratio was between 1 and 5, but it decreased significantly when the ratio increased to 8. The impact of fines content and water content on the mechanical properties was relatively limited. Also, the ${\mathrm{M}}_{\mathrm{R}}\text{-}\theta$ model and Rahman and Erlingsson model showed good fitting performance for resilient modulus and permanent strain, respectively. Finally, neuroevolution of augmenting topologies (NEAT) was used to develop a machine learning model for predicting the resilient modulus of waste rocks, based on 265 data sets. The model showed reliable accuracy, simple topology, and high generalizability capacity. The findings described in this article should be beneficial for the valorization of waste rocks in pavement engineering.