Jigsaw-fit blocks are highly fractured rocks of up to tens of meters wide, associated to volcanic debris avalanches and debris flows, traveling long distances (km) from the volcano edifice. Despite the mass movement long runout and agitated motion, jigsaw-fit blocks are found on their deposits with no apparent disaggregation and with occasional thin matrix facies filling the jigsaw cracks. The mechanisms behind the fragments constrained disaggregation and matrix infilling remain unclear and are limited to field observations. The rheology of granular flows suggests that segregation mechanisms should prevail and high fragmentation rates result from internal shearing and intense inter-granular collisions, challenging the theorized kinematics associated to the frustrated disaggregation of jigsaw-fit blocks. We study experimentally for the first time the segregation and disaggregation processes of analogue jigsaw-fit blocks within a granular flow as a function of fragmentation patterns and fragments density. We found that disaggregation appears regardless of the fragmentation pattern and its initiation is conditioned by the fragment density, occurring faster in fragments lighter than the moving granular media. Our results demonstrate that jigsaw-fit blocks remain united for a short period of shearing, but separate as a result of the fragments rotation and eventual particle infilling. We predict that this work sets a starting point for reviewing the interpretation of jigsaw-fit blocks in debris avalanche deposits, allowing the inference of kinematic features from the fragments configuration and its level of disaggregation.

The granular column collapse consists on the release of a granular volume let to deform or collapse under self-weight until eventually reaching a temporary or permanent stable deposit. Similar to a dam-break in fluid mechanics or a slump test in civil engineering, this configuration was first utilized by the granular media community in 2004. Since then, the granular column collapse has become a benchmark configuration for studying the mobility of granular flows, thanks to its easy setup and reproducibility, and captured rapidly the attention of a wider range of scientific fields working with granular materials. This review covers more than two decades, and even more, of studies employing the granular column collapse as means to understand or describe the motion of grains and their interaction with ambient fluids or gases. This review covers the wide range of fields where the column collapse has been used and includes a database with the collection of experimental works. The aim is to present the questions already answered and summarize the lessons learned from these experimental models. The wealth of applications where the granular column has been used demonstrates how this simple yet rich configuration is proving valuable for validating existing and future particle-based numerical methods.

Micro-scale mechanisms, such as inter-particle and particle-fluid interactions, govern the behaviour of granular systems. While particle-scale simulations provide detailed insights into these interactions, their computational cost is often prohibitive. At a recent Lorentz Center Workshop on “Machine Learning for Discrete Granular Media”, researchers explored how machine learning approaches can aid the development of constitutive laws and efficient data-driven surrogates for granular materials while also addressing uncertainty quantification. Attended by researchers from both the granular materials (GM) and machine learning (ML) communities, the workshop brought the ML community up to date with GM challenges. This position paper emerged from the workshop discussions. In this position paper, we define granular materials and identify seven key challenges that characterise their distinctive behaviour across various scales and regimes–ranging from gas-like to fluid-like and solid-like. Addressing these challenges is essential for developing robust and efficient models for the digital twinning of granular systems in various industrial applications. To showcase the potential of ML to the GM community, we present classical and emerging machine/deep learning techniques that have been, or could be, applied to granular materials. We reviewed sequence-based learning models for path-dependent constitutive behaviour, followed by encoder-decoder type models for representing high-dimensional data in reduced spaces. We then explore graph neural networks and recent advances in neural operator learning. The latter captures the emerging field evolution of interacting particles via efficient latent space representation. Lastly, we discuss model-order reduction and probabilistic learning techniques for high-dimensional parameterised systems, both of which are crucial for quantifying and incorporating uncertainties arising from physics-based and data-driven models. We present a typical workflow aimed at unifying data structures and modelling pipelines and guiding readers through the selection, training, and deployment of ML surrogates for granular material simulations. Finally, we illustrate the workflow’s practical use with two representative examples, focusing on granular materials in solid-like and fluid-like regimes.

In soil testing, assessing physical properties is essential for accurately characterizing sands. However, testing results can vary depending on the experimental procedures used and their implementation. A round-robin exercise facilitates the simultaneous analysis of the reproducibility and replicability of the standard methods used to characterize the properties of a specific material. This paper presents the outcomes of the first inter-laboratory testing initiative (i.e., a round-robin exercise) aimed at assessing the results variability of the physical characterization of a sandy soil. Guamo sand, widely utilized in local research and engineering projects in Colombia, was the focus of this study. 11 national academic laboratories participated in the program, conducting seven replicates of grain size distribution, solids specific gravity, and maximum and minimum void ratio tests. The data provided by all participants were analyzed and interpreted using statistical techniques. The results revealed significant differences between the data collected for each physical property, which can be attributed to the intrinsic variability of this sand’s natural origin and to the use of diverse testing procedures. These comparisons offer valuable practical insights into the discrepancies between the testing methodologies employed by the participants for soil characterization, and they constitute a comprehensive database for future research or practical applications.

The dynamic properties of loose sands under low stresses have been poorly investigated because of the higher order of magnitude of stress levels in terrestrial geotechnical structures. However, low densities and low stresses prevail in the sandy surface deposits of some other rocky planets, making low stress conditions relevant for extra-terrestrial soil mechanics. This is the case of Mars, on the surface of which a seismometer has been placed during the InSight mission. In this context, a dynamic shear rheometer was used to measure the shear modulus and damping ratio of a Martian regolith simulant under very low stresses to improve the interpretation of the InSight dataset on surface materials. This paper also revisits the grain contact stiffness and the overall modulus of a random packing of identical spheres, based on the Hertz-Mindlin contact theory. A micromechanical model accounting for the effects of both grain roughness and slipping in the soil degradation curve is proposed. The results of the model show a good agreement with experimental data, capturing the non-linear transition from low to high-shear strains. The model hence provides a new framework for a better understanding of the behaviour of granular materials in low gravity (extra-terrestrial) conditions.