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Deep neural network and whale optimization algorithm to assess flyrock induced by blasting

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Abstract

A wide variety of artificial intelligence methods have been utilized in the prediction of flyrock induced by blasting. This study focuses on developing a model based on deep neural network (DNN) which is an advanced version of artificial neural network (ANN) for the prediction of flyrock based on the data obtained from the Ulu Thiram quarry that is located in Malaysia. To evaluate and document the success and reliability of the new DNN model, an ANN model based on five different data categories from the established database, was also developed and then compared with the DNN model. Based on the obtained results [i.e. coefficient of determination (R2) = 0.9829 and 0.9781, root mean square error (RMSE) = 8.2690 and 9.1119 for DNN and R2 = 0.9093 and 0.8539, RMSE = 19.0795 and 25.05120 for ANN], a significant increase in predicting flyrock is achieved by developing this DNN predictive model. Then, the DNN model was selected as a function for optimizing flyrock by a powerful optimization technique namely whale optimization algorithm (WOA). The WOA was able to minimize the flyrock resulting from blasting and provide a suitable pattern for blasting operations in mines.

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Acknowledgements

This research was funded by the National Science Foundation of China (41807259), the Natural Science Foundation of Hunan Province (2018JJ3693), the China Postdoctoral Science Foundation funded project (2017M622610) and the Shenghua Lieying Program of Central South University (Principle Investigator: Dr. Jian Zhou).

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Authors and Affiliations

  1. School of Resources and Safety Engineering, Central South University, Changsha, 410083, China

    Hongquan Guo & Jian Zhou

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

    Mohammadreza Koopialipoor

  3. Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam

    Danial Jahed Armaghani

  4. UTM Construction Research Centre, Institute for Smart Infrastructure and Innovative Construction (ISIIC), School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, 81310, Johor Bahru, Johor, Malaysia

    M. M. Tahir

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  1. Hongquan Guo
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Guo, H., Zhou, J., Koopialipoor, M. et al. Deep neural network and whale optimization algorithm to assess flyrock induced by blasting. Engineering with Computers 37, 173–186 (2021). https://doi.org/10.1007/s00366-019-00816-y

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