Reconstruction and generation of 3D realistic soil particles with metaball descriptor.

The morphology of soil particles is crucial in determining its granular characteristics and assembly responses. However, how to introduce accurate and various morphologies of realistic particles in modeling can be challenging, as it often requires time-consuming and costly X-ray Computed Tomography (XRCT). This has led to two prevalent problems in modeling: morphological reconstruction and generation. For reconstruction, we develop a geometric-based Metaball-Imaging algorithm. This algorithm is capable of accurately reconstructing the complex morphologies of realistic particles, including those with concave voids, which cannot be easily represented using other shape descriptors such as the spherical harmonic function. It employs a two-step approach, capturing the main contour of the particles using a series of non-overlapping spheres and then refining surface-texture details through gradient search. Four types of soil particles, hundreds of samples, are applied for evaluations. The result shows good matches on key morphological indicators (i.e., volume, surface area, sphericity, circularity, corey-shape factor, nominal diameter and surface-equivalent-sphere diameter), confirming its reconstruction precision. For generation, we propose the Metaball Variational Autoencoder. Assisted by deep neural networks, this method can generate new 3D particles in Metaball form, while retaining coessential morphological features with parental particles. Additionally, this method allows for control over the generated shapes through an arithmetic pattern, enabling the generation of particles with specific shapes. Two sets of XRCT images different in sample number and geometric features are chosen as parental data. On each training set, one thousand particles are generated for validations. The generation fidelity is demonstrated through comparisons of morphologies and shape-feature distributions between generated and parental particles. Examples are also provided to demonstrate controllability on the generated shapes. With the Metaball-based simulation framework previously proposed by the authors, these methods can incorporate the real shape of particles into simulations, making it possible to simulate a large number of soil particles with varying shapes and behaviors. Together, they have the potential to provide valuable insights into the properties and behavior of actual soil particles. [ABSTRACT FROM AUTHOR]