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Developing a new intelligent technique to predict overbreak in tunnels using an artificial bee colony-based ANN

  • Mohammadreza KoopialipoorEmail author
  • Ebrahim Noroozi Ghaleini
  • Hossein Tootoonchi
  • Danial Jahed Armaghani
  • Mojtaba Haghighi
  • Ahmadreza Hedayat
Original Article

Abstract

The drilling and blasting technique is among the common techniques for excavating tunnels with different shapes and sizes. Nevertheless, due to the dynamic energy involved, the rock mass around the excavation zone experiences damage and reduction in stiffness and strength. One of the most common and important issues that occurs during the tunneling process is the overbreak which is defined as the surplus drilled section of the tunnel. It seems that prediction of overbreak before blasting operations is necessary to minimize the possible damages. This paper develops a new hybrid model, namely, an artificial bee colony (ABC)–artificial neural network (ANN) to predict overbreak. Considering the most important parameters on overbreak, many ABC–ANN models were constructed based on their effective parameters. A pre-developed ANN model was also developed for comparison. In order to evaluate the obtained results of this study, a new system, i.e., the color intensity rating (CIR), was introduced and established to select the best ABC–ANN and ANN models. As a result, the ABC–ANN receives a high level of accuracy in predicting overbreak induced by drilling and blasting. The coefficients of determination (R2) for the ANN and ABC–ANN are 0.9121 and 0.9428, respectively, for training datasets. This revealed that the ABC–ANN model (as a new model in the field of this study) is the best one among the models developed in this study.

Keywords

ABC–ANN ANN Overbreak Blasting 

Notes

Acknowledgements

The authors would like to extend their appreciation to the manager, engineers and personnel of Forum708 (Otagh-MEH), for providing the needed information and facilities that made this research possible. Additionally, the authors would like to express their sincere appreciation to reviewers because of their valuable comments that increased the quality of our paper.

Compliance with ethical standards

Conflict of interest

The authors declare no conflict of interest.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Mohammadreza Koopialipoor
    • 1
    Email author
  • Ebrahim Noroozi Ghaleini
    • 2
  • Hossein Tootoonchi
    • 1
  • Danial Jahed Armaghani
    • 1
  • Mojtaba Haghighi
    • 2
  • Ahmadreza Hedayat
    • 3
  1. 1.Faculty of Civil and Environmental EngineeringAmirkabir University of TechnologyTehranIran
  2. 2.Faculty of Mining and MetallurgyAmirkabir University of TechnologyTehranIran
  3. 3.Faculty of Civil and Environmental EngineeringColorado School of MinesGoldenUSA

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