The ballast of the railway track is constantly changing due to dynamic forces of train traffic that results in the crushing of the rocks. The feasibility to simulate each particle makes the discrete element method (DEM) suitable for the task. Different DEM particle models are introduced in our paper and the adequate one was chosen. This new method is based on crushable convex polyhedral elements and random shape generation via Voronoi tessellation and is implemented in the Yade DEM simulation software. The particle geometry is validated via comparing the simplified shape of natural rough rocks and the randomly generated ones. A 3D scanner was used to digitize the natural rocks. The crushing behaviour is tested as well. The validation of interaction laws and the calibration of the micro parameters is necessary to create a DEM material model with a realistic behaviour. In the calibration process Hummel device is modelled, which provides well measurable parameters for comparing simulation and measurement results.

The size and shape of individual grains, play an important role in the mechanical behavior of granular materials such as the strength and stability of railway ballast. The aim of this research is to study materials from which uniform, reproducible grains with irregular convex geometry can be created by molding and additive manufacturing technologies in order to create reproducible artificial assemblies that can be used in experiments. Packings with determined grain shape results more controlled investigations contrarily to using natural grains with random geometry. Specimens were made from railway ballast materials, materials used in the construction industry, additively manufactured and molded polymers, and certain low-strength materials. Uniaxial compression and bending tests were conducted on these specimens. The mechanical properties of typical railway ballast materials (basalt and andesite) were compared with the properties of artificially produced materials. The results show that for grain reproduction the molding technology is recommended with the use of polyester-crushed stone composite and ceramic powder. Furthermore, the additive manufacturing was recommended with PolyJet or Multi Jet Fusion technology as they have the feasibility to produce grains with similar material properties to the properties of basalt and andesite.

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This study focuses on an old but still unresolved problem of automatically calibrating the constitutive parameters of discrete element models. Instead of the troublesome and time-consuming manual trial-and-error method, which is typical today, the authors suggest using artificial intelligence techniques. A masonry arch is analysed, whose experimental static load–displacement behaviour is known from the literature. An attempt is made to match this behaviour with discrete element models, through finding appropriate quantitative values for the parameters. Two methods (Genetic Algorithm (GA) and Particle Swarm Optimisation (PSO)) are tested and, since PSO turns out to be more reliable, a further improved version, ‘Trust-Based Particle Swarm Optimisation’ (TBPSO), is proposed. The results show that (1) TBPSO quickly leads to suitable alternative parameter sets that make the discrete element model match the behaviour of the real experiments and (2) the optimal values of the parameters strongly depend on the loading velocity and the discretisation method used.