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Machine Learning-Aided Prediction of the Mechanical Properties of Frozen Fractured Rocks

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Abstract

The complexity of fracture geometries impedes reliable prediction of the mechanical properties of frozen fractured rocks. Here, we combine the experimental, numerical, and machine learning methods to predict the uniaxial compressive strength and the Young’s modulus of frozen fractured rocks with five fracture geometries, including persistence factor of ice-filled fractures, spacing between the fractures, as well as inclination angle, thickness, and number of the fractures. We use the results of laboratory uniaxial compression tests to validate the numerical model and the results of two-dimensional particle flow code simulations to train the random forest (RF) models. Our study demonstrates reliable prediction of the uniaxial compressive strength and the Young’s modulus of frozen fractured rocks and compares the prediction performance with the Ramamurthy criterion. We also conduct a sensitivity analysis to reveal dominant geometries and obtain the simplified RF models with three fracture geometries (i.e., persistence factor, inclination angle, and fracture number) for similar prediction accuracy. We finally use additional experimental results to further test the reliability of the simplified RF models. The combined method can be further applied to study other mechanical properties of complex fractured rocks and is particularly suitable for the cases with limited and scattered data from the fractured rocks in experimental and field investigations.

Highlights

  • The experimental and numerical studies show how the complexity of fracture geometries influences the mechanical properties of frozen fractured rocks.

  • The combined experimental, numerical, and machine learning method is developed to predict the mechanical properties of frozen rocks with complex fracture geometries.

  • The sensitivity analysis is used to determine the dominant geometries to simplify the machine learning models for similar prediction accuracy.

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Acknowledgements

This research is supported by the National Research Foundation, Singapore, under its Virtual Singapore R&D Programme (Award No. NRF2019VSG-GMS-001).

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  1. School of Civil and Environmental Engineering, Nanyang Technological University, Singapore, Singapore

    Wenzhao Meng & Wei Wu

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  1. Wenzhao Meng
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  2. Wei Wu
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Correspondence to Wei Wu.

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Meng, W., Wu, W. Machine Learning-Aided Prediction of the Mechanical Properties of Frozen Fractured Rocks. Rock Mech Rock Eng 56, 261–273 (2023). https://doi.org/10.1007/s00603-022-03091-4

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