Engineering with Computers

, Volume 35, Issue 2, pp 627–636 | Cite as

Assessing the suitability of imperialist competitive algorithm for the predicting aims: an engineering case

  • Mao Wu
  • Qingxiang CaiEmail author
  • Tao Shang
Original Article


In the surface and underground mines as well as civil projects, the blasting operation is widely performed for rock breakage. Flyrock is considered as an undesirable environmental impact induced by blasting. Hence, precise prediction of flyrock is a necessary work for safety issue. This research is carried out to evaluate the acceptability of imperialist competitive algorithm (ICA) to approximate the blast-induced flyrock with respect to input parameters including burden, spacing, stemming, weight charge and rock mass rating. In total, 78 blasting operation were investigated and the mentioned parameters, as well as the flyrock values, were measured. In this research work, three ICA-based models, i.e., linear, power, and quadratic models, are introduced. The artificial neural network (ANN) has also been developed by the same data sets and the same input parameters which we used in ICA. The results of the predictors are then evaluated using statistical indicators such as coefficient of determination (R2). Finally, it was proved that the ICA–linear yields a better prediction in comparison with three other models, so that R2 was obtained as 0.954, while the amount of R2 for the ICA–power form, ICA–quadratic form, and ANN models were 0.928, 0.952, and 0.841, respectively.


Blasting Flyrock ICA ANN 



This work was financially supported by the National Key Research and Development Plan (no. 2016YFC0501103), National Natural Science Foundation of China (no. 51774271), National Natural Science Foundation of China (No. 51674245), Natural Science Foundation of Jiangsu Province (no. BK20160259), Priority Academic Program Development of Jiangsu Higher Education Institutions (no. PAPD), and the Fundamental Research Funds for the Central Universities (no. 2014XT01). Thanks to Emate ( for their excellent language service. Thanks to IAMSET for research suggestions. In addition, the authors really appreciate Dr. Mahdi Hasanipanah and Mr. Ali Taherian who allowed us to use his data.


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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of MinesChina University of Mining and TechnologyXuzhouChina
  2. 2.Chinacoal Pingshuo Group Co., Ltd.ShuozhouChina

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