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Rock Mechanics and Rock Engineering

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18.11.2020 | Original Paper

Three-Dimensional Crack Recognition by Unsupervised Machine Learning

verfasst von: Chunlai Wang, Xiaolin Hou, Yubo Liu

Erschienen in: Rock Mechanics and Rock Engineering | Ausgabe 2/2021

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Abstract

Many macrocracks are usually generated during the fracturing of rocks. Elucidating the spatial distribution of cracks provides the basis for understanding crack nucleation and fracture formation in rock mechanics. Considering either a single microcrack or all the microcracks provides a limited interpretation of rock mass failure that is often induced by different macrocracks. Here we recognize macrocracks based on a three-dimensional (3D) crack model, implemented using an unsupervised machine learning algorithm and microcrack coordinates. This approach recognized microcracks that coalesce to form a macrocrack in three dimensions. Rock fracturing was performed using a triaxial loading test, and the coordinate data were obtained via the acoustic emission (AE) technique. The results show that the main macrocracks are distributed throughout the whole granite specimen, and smaller macrocracks form near the unloading surface. The AE-recognized crack pattern was found to be consistent with the actual cracks. The adaptability of the proposed method and the potential research and applications were discussed. This approach provides a means to understand the formation and distribution of rock fractures.

Vorheriger Artikel Fracture Propagation of Rock like Material with a Fluid-Infiltrated Pre-existing Flaw Under Uniaxial Compression

Nächster Artikel Three-Dimensional Numerical Modeling of Grain-Scale Mechanical Behavior of Sandstone Containing an Inclined Rough Joint

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Titel: Three-Dimensional Crack Recognition by Unsupervised Machine Learning
verfasst von: Chunlai Wang
Xiaolin Hou
Yubo Liu
Publikationsdatum: 18.11.2020
Verlag: Springer Vienna
Erschienen in: Rock Mechanics and Rock Engineering / Ausgabe 2/2021
Print ISSN: 0723-2632
Elektronische ISSN: 1434-453X
DOI: https://doi.org/10.1007/s00603-020-02287-w

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