EAS Seminar: Chloé Arson (Cornell CEE)

Description

Fractures as signatures of rock microstructure and climate history

Landscapes encode the history of the climate. For example, saprolite, the intermediate material between rock and soil, plays a critical role in the evolution of topography, nutrient supply, landslide hazards, and the global carbon cycle. In this talk, we present analytical and computational models to predict the propagation of microscopic cracks and metric fractures in rocks under various topographic, tectonic and physical conditions. First, we explain a micro-mechanical model of anisotropic damage induced by the weathering of biotite minerals. The bedrock is modeled as a matrix that contains an anisotropic distribution of microscopic cracks and biotite inclusions endowed with a weathering stress field, called eigenstress. The mechanical properties of the bulk rock are obtained by homogenization. With this model, we conduct a series of finite element simulations of granite bedrock under a sinusoidal topography for a range of biotite orientations. In our simulations, crack growth and stress redistribution are far more sensitive to biotite weathering than to the topographic or regional stresses, which suggests that biotite weathering can dominate the development of bedrock damage. We deploy cohesive zone elements at the boundary of the finite elements and assess the feedback mechanisms between the propagation of fractures and the advancement of the weathering front. Homogenization models such as the one proposed here for weathering allow one to validate or invalidate geomorphological hypotheses, but they require a reliable description of the microstructure by means of inclusion models and inclusion-matrix interaction laws. Physical features that define the inclusions are altered by localizations, e.g., when cracks coalesce, which makes it challenging to define a Representative Elementary Volume that can hold for all loading paths. To overcome this issue, in the second part of this talk, we present Artificial Intelligence (AI) algorithms that detect the microstructure features that are the most significant to describe the mechanical behavior of cracked solids. We train a Variational Auto-Encoder to capture fabric transitions and highlight the statistical descriptors of the crack pattern that best explain the stress field. We also develop a binary classifier based on a Support Vector Machine algorithm to characterize microstructure transitions from an optimal number of geometric features. Detecting such transitions is useful to assess the reliability of estimates of effective properties and to model drastic microstructure changes as a result of natural or induced perturbations, including weathering, erosion, earthquakes, drilling, and subsurface reservoir operations.
 

Bio:
Chloé Arson is a professor in the School of Civil and Environmental Engineering at Cornell University. Prior to Cornell, she was a faculty member at the Georgia Institute of Technology (2012-2023) and at Texas A&M University (2009-2012). She earned her Ph.D. at Ecole Nationale des Ponts et Chaussées (France) in 2009. Dr. Arson’s expertise is in computational geomechanics, with a particular focus on damage and healing mechanics of polycrystalline materials, multi-scale modeling of porous media, and bio-inspired geotechnical design. Her group developed modeling approaches that have allowed a fundamental understanding of synergetic micro-mechanisms in rocks, the prediction of instabilities in geomaterials, and the simulation of concurrent fracture propagation at multiple scales. Homogenization, computational mechanics and Artificial Intelligence (AI) are the pillars of Arson’s work. Inter-disciplinary collaborations have enabled her group to deploy modeling strategies for civil engineering, Earth sciences, mechanical engineering, material sciences, and biology. Dr. Arson delivered the Early Career Address of the American Rock Mechanics Association (ARMA) in 2019, and she received the CAREER and BRITE awards from the U.S. National Science Foundation (NSF), in 2016 and 2021 respectively.