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Predicting roadheader performance by using artificial neural network

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Abstract

With growing use of roadheaders in the world and its significant role in the successful accomplishment of a tunneling project, it is a necessity to accurately predict performance of this machine in different ground conditions. On the other hand, the existence of some shortcomings in the prediction models has made it necessary to perform more research on the development of the new models. This paper makes an attempt to model the rate of roadheader performance based on the geotechnical and geological site conditions. For achieving the aim, an artificial neural network (ANN), a powerful tool for modeling and recognizing the sophisticated structures involved in data, is employed to model the relationship between the roadheader performance and the parameters influencing the tunneling operations with a high correlation. The database used in modeling is compiled from laboratory studies conducted at Azad University at Science and Research Branch, Tehran, Iran. A model with architecture 4-10-1 trained by back-propagation algorithm is found to be optimum. A multiple variable regression (MVR) analysis is also applied to compare performance of the neural network. The results demonstrate that predictive capability of the ANN model is better than that of the MVR model. It is concluded that roadheader performance could be accurately predicted as a function of unconfined compressive strength, Brazilian tensile strength, rock quality designation, and alpha angle R 2 = 0.987. Sensitivity analysis reveals that the most effective parameter on roadheader performance is the unconfined compressive strength.

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Authors and Affiliations

  1. Department of Geology, Faculty of Science, University of Kharazmi, Tehran, Iran

    Armin Salsani & Jahanbakhsh Daneshian

  2. Department of Geology, Faculty of Science, University of Sari, Tehran, Iran

    Shahram Shariati

  3. Department of Mining Engineering, Faculty of Engineering, Tarbiat Modares University, Tehran, Iran

    Abdolreza Yazdani-Chamzini

  4. Department of Mining Engineering, Faculty of Engineering, University of South Tehran, Tehran, Iran

    Mehdi Taheri

Authors
  1. Armin Salsani
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  2. Jahanbakhsh Daneshian
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  3. Shahram Shariati
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  4. Abdolreza Yazdani-Chamzini
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  5. Mehdi Taheri
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Correspondence to Abdolreza Yazdani-Chamzini.

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Salsani, A., Daneshian, J., Shariati, S. et al. Predicting roadheader performance by using artificial neural network. Neural Comput & Applic 24, 1823–1831 (2014). https://doi.org/10.1007/s00521-013-1434-7

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  • DOI: https://doi.org/10.1007/s00521-013-1434-7

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