Feasibility of the indirect determination of blast-induced rock movement based on three new hybrid intelligent models

  • Zhi Yu
  • Xiuzhi ShiEmail author
  • Jian Zhou
  • Dijun Rao
  • Xin ChenEmail author
  • Wenming Dong
  • Xiaohu Miao
  • Timo Ipangelwa
Original Article


The indirect and accurate determination of blast-induced rock movement has important significance in the reduction of ore loss and dilution and in the protection of environment. The present paper aims to predict blast-induced rock movement resulting from the Husab Uranium Mine, Namibia, the Coeur Rochester Mine, USA, and the Phoenix Mine, USA, and three new hybrid models using a genetic algorithm (GA), an artificial bee colony algorithm (ABC), a cuckoo search algorithm (CS) and support vector regression (SVR), namely the GA-SVR, ABC-SVR and CS-SVR models, are proposed. Eight typical blasting parameters rock type, number of free faces, first centerline distance, hole diameter, power factor, spacing, subdrill and initial depth of monitoring were chosen as the input variables to establish the intelligent model, and horizontal blast-induced rock movement (MH) was the output variable after conducting the available analyses of the database. Three performance metrics, including the correlation coefficient (R2), mean square error and variance account for, were used to assess the predictive performances of the aforementioned models. Based on the obtained results, the performance metrics show that the GA-SVR, ABC-SVR and CS-SVR model can provide satisfactory performance in estimating blast-induced rock movement, and GA-SVR model can achieve better results than the GWO-SVR, CS-SVR and ANN models when considering both predictive performance and calculation speed.

Article Highlights

  • Three new hybrid predictive models are proposed (GA-SVR, ABC-SVR and CS-SVR).

  • An more convenient, easily operable and higher accuracy predictive method for blast-induced rock movement determination is presented.

  • The GA-SVR model can provide a higher performance capacity when considering both the predictive performance and the calculation speed.


Blast-induced rock movement Genetic algorithm (GA) Artificial bee colony algorithm (ABC) Cuckoo search algorithm (CS) Support vector regression (SVR) 



This study is supported by the National Natural Science Foundation Project of China (Grant Nos. 51874350 and 41807259), the National Key R&D Program of China (2017YFC0602902), the Fundamental Research Funds for the Central Universities of Central South University (2018zzts217), which are gratefully acknowledged. Moreover, the authors fully acknowledge Blast Movement Technologies, the Uranium Resource Company Limited and the Swakop Uranium Proprietary Limited.


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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Resources and Safety EngineeringCentral South UniversityChangshaChina
  2. 2.Uranium Resource Company Limited, China General Nuclear Power CorporationBeijingChina
  3. 3.Swakop Uranium Proprietary LimitedSwakopmundNamibia

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