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Application of self-organizing map and fuzzy c-mean techniques for rockburst clustering in deep underground projects

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Abstract

One of the main concerns associated with deep underground constructions is the violent expulsion of rock induced by unexpected release of strain energy from surrounding rock masses that is known as rockburst. Rockburst hazard causes substantial damages to the foundation of the structure and equipment and can be a menace to the safety of workers. This study was intended to find the latent relationship between the rockburst-related parameters based on the compiled data samples from deep underground projects using two robust clustering techniques of self-organizing map (SOM) and fuzzy c-mean (FCM). The parameters of maximum tangential stress, uniaxial compressive strength, uniaxial tensile strength, and elastic energy index were considered as input parameters. SOM model could classify data samples into four distinct classes (clusters), and the rockburst intensities were identified precisely. FCM also proved its performance in clustering task with high convergence speed and acceptable accuracy. Having a comparison, the results of SOM and FCM models were compared with ones calculated from five empirical criteria of Russenes, Hoek, tangential stress, elastic energy index, and rock brittleness coefficient. At best, the empirical criteria of Hoek and tangential stress coefficient could predict rockburst intensity with the accuracy of 56.90%. By analyzing the SOM results as the best model, it was turned out that the maximum tangential stress around the openings has a crucial role in rockburst clustering and has the most influence on the occurrence of strong and moderate rockburst types. Hence, it was recommended as a possible solution to control these types of rockbursts by optimizing the diameter and shape of the underground openings.

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Authors and Affiliations

  1. School of Civil, Environmental and Mining Engineering, The University of Adelaide, Adelaide, SA 5005, Australia

    Roohollah Shirani Faradonbeh & Abbas Taheri

  2. Faculty of Mining and Metallurgical Engineering, Urmia University of Technology, Urmia, Iran

    Sina Shaffiee Haghshenas & Reza Mikaeil

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  1. Roohollah Shirani Faradonbeh
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  2. Sina Shaffiee Haghshenas
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  3. Abbas Taheri
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  4. Reza Mikaeil
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Correspondence to Roohollah Shirani Faradonbeh.

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Shirani Faradonbeh, R., Shaffiee Haghshenas, S., Taheri, A. et al. Application of self-organizing map and fuzzy c-mean techniques for rockburst clustering in deep underground projects. Neural Comput & Applic 32, 8545–8559 (2020). https://doi.org/10.1007/s00521-019-04353-z

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