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Rock Penetrability Classification Using Artificial Bee Colony (ABC) Algorithm and Self-Organizing Map

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Abstract

Penetrability is a dominant factor in selecting the suitable drilling method, adjustment of machine parameters and tool wear analysis. In this paper, it is aimed to classify the rock penetrability using Artificial Bee Colony (ABC) algorithm as one of the powerful meta-heuristic algorithms and Self-Organizing Map (SOM) as one of the precise scientific tools. For this purpose, eight different rocks from open pit mines and highway slopes were classified into four separate clusters according to physical and mechanical properties of rock including uniaxial compressive strength, mean Mohs hardness, Young modulus and Schimazek’s F-abrasivity. To evaluate and validate the obtained clusters, field studies were performed and the drilling rate of studied rocks was measured. The results of field study were compared with the results of ABC algorithm and SOM. The results showed that, the studied rocks were reliably classified with respect to their drilling rate. It is concluded that applied techniques can be used for solving the complex rock engineering problems specially in engineering classification of rocks.

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Acknowledgements

The authors would like to express their appreciation to Professor Mahdi Ghaem for his helpful advices and technical support.

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Authors and Affiliations

  1. Department of Mining and Metallurgical Engineering, Urmia University of Technology, Urmia, Iran

    Reza Mikaeil & Sina Shaffiee Haghshenas

  2. Department of Mining Engineering, Isfahan University of Technology, 84156-83111, Isfahan, Iran

    Seyed Hadi Hoseinie

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  1. Reza Mikaeil
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  2. Sina Shaffiee Haghshenas
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  3. Seyed Hadi Hoseinie
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Correspondence to Sina Shaffiee Haghshenas.

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Mikaeil, R., Haghshenas, S.S. & Hoseinie, S.H. Rock Penetrability Classification Using Artificial Bee Colony (ABC) Algorithm and Self-Organizing Map. Geotech Geol Eng 36, 1309–1318 (2018). https://doi.org/10.1007/s10706-017-0394-6

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