Harnessing MEMS sensors and statistics to unravel rock fracture

Abstract

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This study focuses on obtaining differences in rock fracture surface morphology under various loading directions and speeds to infer rock damage mechanics by using micro-electro–mechanical system (MEMS) sensors, which can measure stress, strain, and displacement during loading accurately, providing detailed data for understanding the rock fracture mechanism for physics-informed statistics. Statistical variables analyze directional angle samples of the normal vector central line. The deviation normal distribution coefficient (DNDC) for rock fracture surface normal vectors is defined by the kurtosis coefficient. Brazilian splitting tests calculate the DNDC for Brazilian disk fracture surfaces. The variation in the DNDC with a measurement scale distinguishes morphological differences. Three results are obtained: the DNDC has a scale effect; loading the specimen in another direction before compression causes internal damage; and different loading speeds do not significantly change the DNDC. This research holds promise for a better understanding of rock fractures.

Keywords

• micro-electromechanical system
• physics-informed statistics
• rock mechanics
• rock fracture surface
• kurtosis coefficient
• normal distribution