A prediction model of the joint roughness coefficient based on Gaussian process regression

ZHENG Kexin; WU Yiping; LI Jiang; MIAO Fasheng; KE Chao

doi:10.19509/j.cnki.dzkq.tb20230113

Volume 43 Issue 4

Jul. 2024

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Article Navigation > Bulletin of Geological Science and Technology > 2024 > 43(4): 252-261

ZHENG Kexin, WU Yiping, LI Jiang, MIAO Fasheng, KE Chao. A prediction model of the joint roughness coefficient based on Gaussian process regression[J]. Bulletin of Geological Science and Technology, 2024, 43(4): 252-261. doi: 10.19509/j.cnki.dzkq.tb20230113

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ZHENG Kexin, WU Yiping, LI Jiang, MIAO Fasheng, KE Chao. A prediction model of the joint roughness coefficient based on Gaussian process regression[J]. Bulletin of Geological Science and Technology, 2024, 43(4): 252-261. doi: 10.19509/j.cnki.dzkq.tb20230113

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A prediction model of the joint roughness coefficient based on Gaussian process regression

doi: 10.19509/j.cnki.dzkq.tb20230113

1.
Faculty of Engineering, China Universityof Geosciences (Wuhan), Wuhan 430074, China
2.
Information Center, Department of Natural Resources of Hubei Province, Wuhan 430071, China

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Author Bio:
ZHENG Kexin, E-mail: zkx1998@cug.edu.cn
Corresponding author: WU Yiping, E-mail: ypwu@cug.edu.cn
Received Date: 06 Mar 2023
Accepted Date: 19 Apr 2023
Rev Recd Date: 18 Apr 2023

Abstract

Abstract

Objective
Estimating the joint roughness coefficient (JRC) is essential for evaluating the mechanical properties of a rock mass. Due to the limitation of a single statistical parameter for characterizing morphology, JRC values estimation by a single statistical parameter may produce a sufficiently unreliable result.
Methods
To address the existing challenges in determining JRC values, a model based on Gaussian process regression (GPR) combined with principal component analysis (PCA) was proposed for the quantitative evaluation of JRC. Notably, eight parameters were selected as indicators for the comprehensive expression of the rock joint roughness. To analyse the model's performance, a publicly available dataset of 112 rock joint profiles was used as an example, of which 95 were chosen as training samples and 17 were chosen as validation samples. The reliability of the model was verified by comparing the predicted results with the measured JRC values.
Results
The results show that the derived GPR model demonstrates promising performance (R²=0.972, MSE =0.517) for estimation of JRC values, indicating the high applicability of the model in constructing implicit relationships between multiple statistical parameters and JRC values even under small sample conditions.
Conclusion
In general, the GPR model may provide a new way of estimating JRC values with artificial intelligence.
- rock joints,
- roughness,
- Gaussian process regression,
- statistical parameter,
- prediction

The authors declare that no competing interests exist.

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References(44)

References

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A prediction model of the joint roughness coefficient based on Gaussian process regression

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