Predicting the cyclic behavior of granular materials remains a major engineering challenge that is not yet sufficiently captured by existing computer models developed in the last few decades. Cyclic behavior of granular materials is critical in a large number of applications, including soil behavior under earthquake loading and clean energy harvesting offshore. A missing link in the current models is the inclusion of internal granular structure (fabric), which is known to control the overall behavior in granular materials, especially under cyclic loads where the material experiences load reversals. The objective of this research is to extend the predictive capability of computer models for granular materials subjected to cyclic loading by carefully investigating the effects of the fabric. This research will use cutting edge computational, experimental and in situ techniques to develop next-generation models for granular materials such as sands. This research is a collaboration between Caltech and the Norwegian Geotechnical Institute. Furthermore, this project will contribute to the training of students at the undergraduate and graduate levels. Undergraduate students will be involved via the summer undergraduate research fellowship at Caltech. This research will also have significant impact on continuing to expand the international educational infrastructure in geotechnical engineering, especially between the United States and Europe.

The working hypothesis of this research is that continuum behavior is encoded at the scale where neighboring grains interact. These interactions establish a stress path network, or fabric, that adapts dynamically to changing loading conditions; giving rise to meso-scale features like force chains, which are unique to granular materials. The upshot of this is that the evolution of fabric induces continuous change in the meso-scale and the continuum response of the material. The dearth of continuum models capable of predicting accurately the cyclic behavior of granular materials could be attributed to a general lack of understanding of how fabric evolves as a function of cyclic loading and how it influences the granular material behavior. This research aims to fill this gap by accomplishing three central research tasks: 1: Characterization and reconstitution of inherent fabric; 2: Modeling fabric evolution in cyclic stress paths; 3: Continuum description and validation of fabric evolution relations. This research will motivate the design of new experimental apparatuses and inspire a new generation of predictive constitutive models for granular materials.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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California Institute of Technology
United States
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