Research

Granular materials exhibit regime transitions between solid-like, fluid-like, and gas-like behaviours, influenced by factors such as loading conditions, particle size distribution, density, and material strength. The transition between solid-like and fluid-like states is particularly significant, as it underpins critical phenomena like landslides, coastal erosion and sediment transport. It is also vital for optimising industrial processes and designing rovers for space exploration. Despite extensive research, the fundamental mechanisms governing these transitions remain poorly understood. This knowledge gap limits our ability to reliably predict geological events and optimise engineering and manufacturing processes. This project aims to advance our understanding of regime transitions in granular materials using novel laboratory micromechanical tests, coupled modelling of solid-fluid interaction, constitutive modelling and large-deformation analysis. Our consortium brings together expertise across diverse disciplines, including advanced experimental testing, numerical and physical modelling, geological engineering, robotics, and software development. All participating organisations will contribute to the research activities by leading research work packages, staff secondment and/or providing technical/infrastructure support. Alongside research, the project will facilitate staff exchange through secondments and networking and train young researchers in research and soft skills by training schools. These activities will help form lasting collaborations globally and train the next generation of researchers and engineers driving technological advancements in academia and industry. The project will also encourage knowledge exchange between universities and industry, enabling the real-world application of new knowledge in software and test equipment development, and the creation of new spin-out companies offering services in advanced experiments and numerical modelling.

Our proposed research initiative seeks to propel machine learning into the forefront of geotechnical engineering, with a vision to address critical challenges and revolutionise the field for the betterment of society. The overarching goals of our project align with the need to confront uncertainty, combat climate change through zero carbon emission strategies, address soil parameter heterogeneity, expedite finite element (FE) calculations e.g., for reliability analyses, and enhance design efficiency to reduce material consumption, particularly in the context of concrete. By undertaking this multidimensional approach, our research aims not only to apply machine learning in geotechnical engineering but to fundamentally transform the field, ushering in a new era of efficiency, sustainability and resilience. Through collaboration and innovation, we aspire to make machine learning an integral and indispensable tool for addressing the complex challenges faced by geotechnical practitioners in the 21st century.

Geohazards, such as rock avalanches, landslides and debris flows, are commonly recoganized as the slow-to-rapid gravitationallydriven processes that typically occur in mountain regions, such as Alps in Europe, Himalaya in Asia, Rocky in North Americas and Snowy in Australia, possessing potential hazards societies. With the advancement of computer science, numerical simulations of geohazards have become crucial in the modern geomechanics and geotechnical engineering. The fragmentation of current research into local national projects often falls short in comprehensive understanding of the evolution mechanisms. This gap results in a grey area in modern numerical methods for high-fidelity simulations, limiting accessibility for both scientific researchers and engineering practitioners. MONUGEO brings together the complementary expertise of our consortium members to develop a better understanding of triggering initiation, run-out and deposition (and/or interaction with protective obstacles) processes, and in turn to produce the ground-breaking numerical tools for the high-fidelity predictions. Our international and interdisciplinary consortium will also prefer to an integrated research approach, involving laboratory experiments, scaled centrifuge physics modelling tests, and region-scale application with geological survey. This integrated methodology will serve to validate our developed computing paradigms and numerical toolbox, and to apply them to realistic scenario.