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A roadmap is presented to transition seamlessly from an image to a predictive computational model for granular materials. So far, constitutive modelling in granular materials has been based on macroscopic experimental observations. Here, the point of departure is the basic granular scale where kinematics, contact forces and fabric control the macroscopic mechanical behaviour of the material. New computational and analytical tools are presented that allow for more accurate measurement of kinematics and inference of contact forces, directly from imaging tools (e.g. highenergy tomography). These grain-scale data are then used to construct powerful multiscale models that can predict the emergent behaviour of granular materials, without resorting to phenomenology, but can rather directly unravel the micro-mechanical origin of macroscopic behaviour. The aim of these tools is to furnish a 'tomography-to-simulation' framework, where experimental techniques, imaging procedures, and computational models are seamlessly integrated. These integrated techniques will help define a new physics-based approach for modelling and characterisation of granular soils in the near future.

We introduce an improved version of a computational algorithm that "clones"/generates an arbitrary number of new digital grains from a real sample of real digitalized granular material. Our improved algorithm produces "cloned" grains that more accurately approach the morphological features displayed by their parents. Now, the "cloned" grains were also included in a Discrete Element Method simulation of a tri-axial test and showed similar mechanical behavior compared to the displayed by the original (parent) sample. Thus, the present work is divided in four parts. First, we compute multivariable probability density functions (PDF) from the parents' morphological parameters (morphological DNA), i.e., aspect ratio, roundness, volume-surface ratio, and particle diameter. Second, an improved, now parallelized and better tuned version of the Geometric Stochastic Cloning (GSC) algorithm [13], which is based on the aforementioned multivariable distributions, and that, in the same way, introduces an enhanced radii sampling process, as well as a new quality control test based on the volume-surface ratio is discussed. Third, morphological DNA of the grains (i.e., aspect ratio, roundness, volume-surface ratio and particle diameter) is also extracted from the new "cloned" grains and compared to the one obtained from the parent sample. Fourth, clones and parents are subjected to a tri-axial compression tests using a Level Set (LS) in Discrete Element scheme (3DLS-DEM), and then, compared in terms of their mechanical response. Finally, the error of the "clones" in the morphology and mechanical behavior is analyzed and discussed for future improvements.

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