A Novel Approach for Blast-Induced Fly Rock Prediction Based on Particle Swarm Optimization and Artificial Neural Network

  • Navdeep KumarEmail author
  • Balmukund Mishra
  • Vikram Bali
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 34)


Fly-rocks are the excessive rock fragments. There random throw from a blast can travel a large distance which may be beyond the blast safety area. This process of the blasting operation results in human injuries, fatalities, and structural damage. In this research work, a method is proposed to predict the fly-rocks. This approach is built on the mixture of particle swarm optimization and artificial neural network. Here ANN is used to predict fly-rock distance. Generally, ANN is used as one of the forceful areas of research in advanced and varied applications of science. ANN has the ability to right to map the input to output patterns. Also, it utilizes all influential parameters in case of prediction of fly-rock distance. But, there are still some limitations concerned to ANN, i.e., the rate of slow learning and getting stuck in local minima. This research work offers a mix PSO-ANN predictive model for fly-rock prediction. The results of the developed model are compared to the results of ICA-ANN, BP-ANN, empirical equation, and multivariate regression analysis (MRA). The parameters for comparison are root mean square error, coefficient of determination (R2), and least cost. These parameters are firstly calculated by comparing testing and training data from ANN. These parameters are then compared with that of the existing methods, i.e., ICA-ANN, BP-ANN, empirical equation, and multivariate regression analysis (MRA). MATLAB R2013a is used as an implementation platform using general MATLAB toolbox.


Artificial neural network Imperialist competitive algorithm Flies rock Blasting 


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© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Computer Science DepartmentP.I.E.T SamalkhaPanipatIndia

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