Roughness coefficient of rock discontinuities based on random forest regression analyses

LI Wenbin; FENG Wenkai; HU Yunpeng; ZHOU Yongjian; CHEN Kai; LIU Yun

doi:10.16030/j.cnki.issn.1000-3665.202110048

2023 Vol. 50, No. 1

Article Contents

Article navigation > Hydrogeology & Engineering Geology > 2023 > 50(1): 87-93

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LI Wenbin, FENG Wenkai, HU Yunpeng, ZHOU Yongjian, CHEN Kai, LIU Yun. Roughness coefficient of rock discontinuities based on random forest regression analyses[J]. Hydrogeology & Engineering Geology, 2023, 50(1): 87-93. doi: 10.16030/j.cnki.issn.1000-3665.202110048

Citation:

LI Wenbin, FENG Wenkai, HU Yunpeng, ZHOU Yongjian, CHEN Kai, LIU Yun. Roughness coefficient of rock discontinuities based on random forest regression analyses[J]. Hydrogeology & Engineering Geology, 2023, 50(1): 87-93. doi: 10.16030/j.cnki.issn.1000-3665.202110048

Roughness coefficient of rock discontinuities based on random forest regression analyses

1.
State Key Laboratory of Geohazard Prevention and Geoenvironment Protection （Chengdu University of Technology）, Chengdu, Sichuan　610059, China
2.
He’nan Xinhua Wuyue Pumped Storage Power Generation Co. Ltd., Xinyang, He’nan　465450, China

More Information

Corresponding author: FENG Wenkai, fengwenkai@cdut.cn

Received Date: 28 October 2021

Revised Date: 06 December 2021

Accepted Date: 21 February 2022

Publish Date: 15 January 2023

Abstract

Abstract

The peak shear strength of the discontinuities can be estimated quickly by the roughness of the discontinuities. However, it is difficult to quantify the roughness of the structural surface using the single statistical parameter. In order to improve the prediction accuracy of the standard discontinuities roughness, eight statistical parameters in the aspects of undulating degree and trace length of 112 structural profile curves are collected, and the method of cross-validation of random forest regression model is used to evaluate the importance of statistical parameters. The evaluation results show that the importance of six statistical parameters, including the maximum undulation, undulation height standard deviation, mean undulating angle, undulating angle standard deviation, mean relative undulation rave and roughness profile index, accounts for 93.2%, and the regression fitting coefficient tends to be stable. Based on the importance assessment results, a random forest regression model is established. The model prediction results fitting excellence is up to 98.1%, showing the excellent prediction results. Compared with the traditional linear regression results, such as the results of the slope-based mean square root, structural function and roughness profile index, the random forest regression model has higher accuracy, smaller error and better fit. The random forest regression model is more suitable for structural roughness inversion.
- random forest /
- roughness /
- rock structure surface /
- statistical parameters /
- importance assessment

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References

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