Neural Computing and Applications

, Volume 24, Issue 7–8, pp 1823–1831 | Cite as

Predicting roadheader performance by using artificial neural network

  • Armin Salsani
  • Jahanbakhsh Daneshian
  • Shahram Shariati
  • Abdolreza Yazdani-Chamzini
  • Mehdi Taheri
Original Article


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 R2 = 0.987. Sensitivity analysis reveals that the most effective parameter on roadheader performance is the unconfined compressive strength.


Roadheader performance Artificial neural network Multiple variable regression Tunneling 


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Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  • Armin Salsani
    • 1
  • Jahanbakhsh Daneshian
    • 1
  • Shahram Shariati
    • 2
  • Abdolreza Yazdani-Chamzini
    • 3
  • Mehdi Taheri
    • 4
  1. 1.Department of GeologyFaculty of Science, University of KharazmiTehranIran
  2. 2.Department of GeologyFaculty of Science, University of SariTehranIran
  3. 3.Department of Mining EngineeringFaculty of Engineering, Tarbiat Modares UniversityTehranIran
  4. 4.Department of Mining EngineeringFaculty of Engineering, University of South TehranTehranIran

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