Microstructural Evolution of Carrara Marble with Complex Strain Histories [dataset]

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Authors: Jack Qidiao
Cited by: 0

Abstract

The rheology of geologic materials is crucial for understanding the dynamics of planetary interiors. However, there have been few experimental studies of geologic materials subjected to cyclical strain, as might be imposed by periodic ice-sheet loading or tidal forcing. To investigate the effect of cyclical strain on microstructural evolution we conducted deformation experiments with the same absolute shear strain magnitude, the same net strain, and different strain paths. We deformed Carrara Marble at high temperature and high pressure, at a constant strain rate, in the Large Volume Torsion Apparatus at Washington University in St. Louis. Thin sections and EBSD maps were used to characterize deformation microstructures, including grain-size, the fraction of material that is dynamically recrystallized, and crystallographic preferred orientation. There is poor correlation between the areal fraction of recrystallized grains and either the net or the absolute shear strain imposed by cyclical loading. However, there is excellent correlation between the percentage of recrystallized grains and the maximum strain for any deformation stage. We conclude that microstructural evolution during complex cyclical loading is dominated by the largest strain increment, regardless of when it occurs during the deformation history. Comparison of microstructures to single-stage deformation experiments indicates that, despite nearly identical total deformation work, the resulting microstructures vary substantially. It demonstrates that strain-path history, rather than total amount of deformation, exerts stronger control on microstructural evolution.

Fields of Study & Subjects

High-pressure geophysics and materials Geological and Geochemical Analysis Rock Mechanics and Modeling

Publication details

Journal / source
Washington University in St. Louis Libraries
Publication date
14 October 2026
Language
EN
Document type
Dataset