Prediction of blast-induced flyrock in Indian limestone minesusing neural networks

Frequency and scale of the blasting events are increasing to boost limestone production. Mines areapproaching close to inhabited areas due to growing population and limited availability of land resourceswhich has challenged the management to go for safe blasts with special reference to opencast mining.The study aims to predict the distance covered by the flyrock induced by blasting using artificial neuralnetwork （ANN） and multi-variate regression analysis （MVRA） for better assessment. Blast design andgeotechnical parameters, such as linear charge concentration, burden, stemming length, specific charge,unconfined compressive strength （UCS）, and rock quality designation （RQD）, have been selected as inputparameters and flyrock distance used as output parameter. ANN has been trained using 95 datasets ofexperimental blasts conducted in 4 opencast limestone mines in India. Thirty datasets have been used fortesting and validation of trained neural network. Flyrock distances have been predicted by ANN, MVRA,as well as further calculated using motion analysis of flyrock projectiles and compared with the observeddata. Back propagation neural network （BPNN） has been proven to be a superior predictive tool whencompared with MVRA.
2014 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. Production and hosting byElsevier B.V. All rights reserved.

Prediction of blast-induced flyrock in Indian limestone mines using neural networks

Frequency and scale of the blasting events are increasing to boost limestone production.Mines are approaching close to inhabited areas due to growing population and limited availability of land resources which has challenged the management to go for safe blasts with special reference to opencast mining.The study aims to predict the distance covered by the flyrock induced by blasting using artificial neural network(ANN) and multi-variate regression analysis(MVRA) for better assessment.Blast design and geotechnical parameters,such as linear charge concentration,burden,stemming length,specific charge,unconfined compressive strength(UCS),and rock quality designation(RQD),have been selected as input parameters and flyrock distance used as output parameter.ANN has been trained using 95 datasets of experimental blasts conducted in 4 opencast limestone mines in India.Thirty datasets have been used for testing and validation of trained neural network.Flyrock distances have been predicted by ANN,MVRA,as well as further calculated using motion analysis of flyrock projectiles and compared with the observed data.Back propagation neural network(BPNN) has been proven to be a superior predictive tool when compared with MVRA.