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Classification and extent determination of rock slope using deep learning

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Geomechanics and Geophysics for Geo-Energy and Geo-Resources Aims and scope Submit manuscript

Abstract

The classification of rock slopes and the determination of rock extent are essential for slope stability analysis. At present, the efficiency of artificial methods is low and affected by subjective factors. A convolution neural network model for rock slope image analysis is established based on the Tensorflow. 80,000 rock slope images are extracted and compressed using convolution and pooling. Then a network model is trained to automatically identify and classify rock slopes. Using the training set and the rock slope image in the test set to test and analyze the model, the training set accuracy rate is 98%, and the test set accuracy rate is 90%, which indicates that the network model after training is robust. Based on the color of different rocks in the slope, the extent of different kinds of rocks in the rock slope is determined using a deep learning regression operation. In order to verify the effect of the algorithm, a standard color rock slope image is selected for a simulation experiment, and the boundary detection is accurate. Finally, the deep learning network model is used to quickly and automatically identify and classify rock slopes. The rock slope information obtained by image identification is imported into independently developed GeoSMA-3D software, which is an important parameter for determining the grade of rock slopes.

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The [DATA TYPE] data used to support the findings of this study have been deposited in the [NAME] repository. All research analysis data are available on demand.

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Acknowledgements

The authors would like to thank Northeastern University in providing access to the software and other facilities. This work was conducted with support from the National Natural Science Foundation of China (Grant Nos. 51474050 and U1602232), the Fundamental Research Funds for the Central Universities(Grant No.17010829), Doctoral Scientific Research Foundation of Liaoning Province (Grant Nos. 20170540304 and 20170520341), State Key Laboratory of Silicate Materials for Architectures (Wuhan University of Technology) (Grant No. SYSJJ2017-08), the research and development project of China construction stock technology (Grant No. CSCEC-2016-Z-20-8) to Dr. Shuhong Wang.

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Authors and Affiliations

  1. School of Resources and Civil Engineering, Northeastern University, Shenyang City, 110819, China

    Pengyu Wang, Shuhong Wang, Chengjin Zhu & Zishan Zhang

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  1. Pengyu Wang
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  2. Shuhong Wang
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  3. Chengjin Zhu
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Correspondence to Shuhong Wang.

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On behalf of all authors, the corresponding author states that there is no conflict of interest. No conflict of interest exists in the submission of this manuscript, and the manuscript is approved by all authors for publication. I would like to declare on behalf of my co-authors that the work described was original research that has not been published previously, and not under consideration for publication elsewhere, in whole or in part. All the authors listed have approved the manuscript that is enclosed.

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Wang, P., Wang, S., Zhu, C. et al. Classification and extent determination of rock slope using deep learning. Geomech. Geophys. Geo-energ. Geo-resour. 6, 33 (2020). https://doi.org/10.1007/s40948-020-00154-0

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