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Overbreak prediction and optimization in tunnel using neural network and bee colony techniques

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Abstract

Overbreak is an undesirable phenomenon in blasting operations. The causing factors of overbreak can be generally divided as blasting and geological parameters. Due to multiplicity of effective parameters and complexity of interactions among these parameters, empirical methods may not be fully appropriated for blasting pattern design. In this research, artificial neural network (ANN) as a powerful tool for solving such complicated problems is developed to predict overbreak induced by blasting operations in the Gardaneh Rokh tunnel, Iran. To develop an ANN model, an established database comprising of 255 datasets has been utilized. A three-layer ANN was found as an optimum model for prediction of overbreak. The coefficient of determination (R2) and root mean square error (RMSE) values of the selected model were obtained as 0.921, 0.4820, 0.923 and 0.4277 for training and testing, respectively, which demonstrate a high capability of ANN in predicting overbreak. After selecting the best model, the selected model was used for optimization purpose using artificial bee colony (ABC) algorithm as one of the most powerful optimization algorithms. Considering this point that overbreak is one of the main problems in tunneling, reducing its amount causes to have a good tunneling operation. After making several models of optimization and variations in its weights, the optimum amount for the extra drilling was 1.63 m2, which is 47% lower than the lowest value (3.055 m2). It can be concluded that ABC algorithm can be introduced as a new optimizing algorithm to minimize overbreak induced by tunneling.

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Acknowledgements

The authors would like to express their sincere appreciation to the anonymous reviewers for their valuable and constructive suggestions.

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Authors and Affiliations

  1. Faculty of Civil and Environmental Engineering, Amirkabir University of Technology, Tehran, 15914, Iran

    Mohammadreza Koopialipoor

  2. Faculty of Mining and Metallurgy, Amirkabir University of Technology, Tehran, Iran

    Ebrahim Noroozi Ghaleini

  3. Faculty of Mining and Metallurgy, Amirkabir University of Technology, Tehran, Iran

    Mojtaba Haghighi

  4. Department of Computer Science, Annai Vailankanni Arts and Science College, (Affiliated to Bharathidhasan University), Thanjavur, Tamilnadu, 613007, India

    Sujith Kanagarajan

  5. Faculty of Mining and Metallurgy, Amirkabir University of Technology, Tehran, Iran

    Parviz Maarefvand

  6. Centre of Tropical Geoengineering (GEOTROPIK), Faculty of Civil Engineering, Universiti Teknologi Malaysia, 81310, Johor, Malaysia

    Edy Tonnizam Mohamad

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  1. Mohammadreza Koopialipoor
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  2. Ebrahim Noroozi Ghaleini
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  3. Mojtaba Haghighi
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Correspondence to Mohammadreza Koopialipoor.

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Koopialipoor, M., Ghaleini, E.N., Haghighi, M. et al. Overbreak prediction and optimization in tunnel using neural network and bee colony techniques. Engineering with Computers 35, 1191–1202 (2019). https://doi.org/10.1007/s00366-018-0658-7

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