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Rock Discontinuities Identification from 3D Point Clouds Using Artificial Neural Network

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Abstract

Rock discontinuities fundamentally impact the mechanical and hydraulic behaviors of a rock mass, and thus it is a critically important task to characterize the geometrical parameters of these rock discontinuities. To measure the discontinuity orientation more accurately and efficiently, two well-known point clouds were taken as cases (a cube and a road cut), and an artificial neural network (ANN)-an machine learning algorithm-was established to identify discontinuities from point clouds through learning a small number of training samples, which had been manually selected from the raw point clouds. Four attributes associated with geometrical features of point clouds were specified as input parameters, namely, point XYZ-coordinates, point normal, point curvature, and point density. Two main groups-discontinuity and non-discontinuity-were produced in the output layer, and the number of the discontinuity groups greatly depended on the sets of discontinuities in the real situation. Using principal component analysis (PCA) and density-based spatial clustering of applications with noise (DBSCAN), single discontinuities were extracted from the group discontinuities which were obtained using ANN, and the corresponding orientations were calculated. The results obtained with the proposed method in this study matched the field surveys and results calculated by a modified region-growing algorithm. The computational efficiency was significantly enhanced using the proposed method, only taking several seconds to process a huge data. More importantly, the accuracy of discontinuity detection was greatly improved by specifying the noise data as the non-discontinuity groups during training samples selection in ANN. The ANN approach does not require the engineers have a strong professional background in computer programming, which simplified the detection and characterization process of rock discontinuity. Furthermore, an APP-named DisDetANN-was developed to implement the rock discontinuity detection based on the proposed ANN model, and the full code of the DisDetANN has been freely shared on GitHub.

Highlights

  • An artificial neural network was created by machine learning to detect group discontinuities from point clouds.

  • Point coordinate, normal, curvature, and density were considered in input layers of the artificial neural network.

  • A clustering algorithm was employed to subdivide group discontinuities into single discontinuities.

  • Both efficiency and accuracy of the discontinuity detection was improved by the proposed approach.

  • An APP and full codes of the proposed method were freely made available to the engineering community.

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Availability of data and material

The data and materials that support the findings of this study are available from the first and corresponding author, Yunfeng Ge, upon reasonable request.

Code availability

The codes that support the findings of this study are available at GitHub (https://Github.com/DisDetANN/DIsDetANNcode).

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Acknowledgements

This work was jointly supported by the National Key R&D Program of China (No. 2017YFC1501303), and the National Natural Science Foundation of China (No. 42077264). Thanks to Dr. Matt Lato of the Queen’s University in Canada and Dr. Adrián J. Riquelme of the Universidad de Alicante in Spain to share the point clouds. The authors’ special appreciation goes to Mr. Ross Haselhorst and Mr. Lea Hickerson for language editing.

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Authors and Affiliations

  1. Faculty of Engineering, China University of Geosciences, Wuhan, 430074, Hubei, China

    Yunfeng Ge, Bei Cao & Huiming Tang

  2. Three Gorges Research Center for Geo-Hazard, China University of Geosciences, Wuhan, 430074, China

    Huiming Tang

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  1. Yunfeng Ge
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  2. Bei Cao
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  3. Huiming Tang
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Contributions

YG: conceptualization, data curation, formal analysis, investigation, methodology, resources, software, supervision, validation, visualization, writing-original draft. BC: data curation, software, investigation, validation, visualization, writing-original draft. HT: conceptualization, funding acquisition, project administration, supervision.

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Correspondence to Yunfeng Ge.

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Ge, Y., Cao, B. & Tang, H. Rock Discontinuities Identification from 3D Point Clouds Using Artificial Neural Network. Rock Mech Rock Eng 55, 1705–1720 (2022). https://doi.org/10.1007/s00603-021-02748-w

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  • DOI: https://doi.org/10.1007/s00603-021-02748-w

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