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Identification of rock mass discontinuity from 3D point clouds using improved fuzzy C-means and convolutional neural network

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Abstract

Accurately obtaining rock mass discontinuity information holds particular significance for slope stability analysis and rock mass classification. Currently, non-contact measurement methods have increasingly become a supplementary means to traditional techniques, especially in hazardous and inaccessible areas. This study introduces an innovative semi-automatic method to identify discontinuities from point clouds. A modified convolutional neural network, AlexNet, was established to identify discontinuity sets. The network consists of five convolutional layers and three fully connected layers, utilizing 1 × 3 normal vectors computed by K-nearest neighbor and principal component analysis as input and generating an output value “i” that represents the identified discontinuity set associated with the “i” category. Learning samples for network training were randomly selected from point clouds and automatically categorized using the improved fuzzy C-means (FCM) based on particle swarm optimization (PSO). The orientations of individual discontinuities, identified from the discontinuity set using hierarchical density–based spatial clustering of applications with noise, were calculated. Two outcrop cases were employed to validate the efficacy of the proposed method, and parameter analysis was conducted to determine optimal parameters. The results demonstrated the reliability of the method and highlighted improvements in automation and computational efficiency.

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Data Availability

The data that support the findings of this study are available from the corresponding author, Bei Cao, upon reasonable request.

Code availability

The codes that support the findings of this study are available at GitHub (https://github.com/rockslopeworking/Rockmass-discontinuity).

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Acknowledgements

Thanks to Dr. Siefko Slob for sharing the point clouds of case A. The raw data of case B was obtained from the Rockbench repository. The authors kindly appreciated M. Lato, J. Kemeny, R.M. Harrap, and G. Bevan for establishing the Rockbench repository. The authors’ special appreciation goes to editors and anonymous reviewers for valuable comments.

Funding

This work was supported by the National Natural Science Foundation of China (No. 41974148), the Natural Resources Science and Technology Project of Hunan Province (Grant No.2022–01), the Research Foundation of the Department of Natural Resources of Hunan Province (Grant No. 20230101DZ), and the Science and Technology Research and Development Project of China Railway Co., LTD (No. 2022-Special-07).

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Authors and Affiliations

  1. School of Geosciences and Info-Physics, Central South University, Changsha, 410083, China

    Guangyin Lu, Bei Cao, Xudong Zhu, Dongxin Bai, Chuanyi Tao & Yani Li

  2. National Engineering Research Center of High-Speed Railway Construction Technology, Changsha, 410083, China

    Guangyin Lu, Bei Cao, Xudong Zhu, Dongxin Bai & Chuanyi Tao

  3. Hunan Key Laboratory of Nonferrous Resources and Geological Hazards Exploration, Changsha, 410083, China

    Guangyin Lu, Bei Cao, Xudong Zhu, Dongxin Bai, Chuanyi Tao & Yani Li

  4. CSCEC-TAISEI CONSTRUTION. LTD, Beijing, 100037, China

    Zishan Lin

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  1. Guangyin Lu
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Contributions

Conceptualization: Guangyin Lu, Bei Cao; methodology: Guangyin Lu, Bei Cao; formal analysis: Guangyin Lu, Zishan Lin; investigation: Bei Cao, Xudong Zhu; writing—original draft: Bei Cao; writing—review and editing: Bei Cao, Xudong Zhu, Zishan Lin, Chuanyi Tao, Yani Li; data curation: Bei Cao, Xudong Zhu; visualization: Guangyin Lu, Xudong Zhu; software: Bei Cao, Xudong Zhu; validation: Xudong Zhu, Zishan Lin, Dongxin Bai, Chuanyi Tao; funding acquisition: Guangyin Lu; resources: Guangyin Lu; supervision: Dongxin Bai, Yani Li;

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Correspondence to Bei Cao.

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Lu, G., Cao, B., Zhu, X. et al. Identification of rock mass discontinuity from 3D point clouds using improved fuzzy C-means and convolutional neural network. Bull Eng Geol Environ 83, 159 (2024). https://doi.org/10.1007/s10064-024-03658-1

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