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

Abstract

a b s t r a c tFrequency 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.

Three hybrid intelligent models in estimating flyrock distance resulting from blasting

“…In another research, the parameters including density, explosive density hole diameter, and confinement state were used by McKenzie [41] for prediction of flyrock and particle (rock) size. A new empirical equation to assess flyrock was used by Trivedi et al [42]. For the development of this model, 95 explosive data sets were used and an equation based on specific charge, charge concentration, rock strength, burden, stemming length and rock quality designation was proposed to estimate flyrock.…”

Application of two intelligent systems in predicting environmental impacts of quarry blasting

“…In another empirical study, Trivedi et al (2014) proposed an equation to predict flyrock distance using multivariate regression analysis. To do this, they monitored 95 blasting operations of four opencast limestone mines in India and relevant blasting parameters were recorded.…”

Application of cuckoo search algorithm to estimate peak particle velocity in mine blasting

“…Environmental problems produced from blasting operation, such as ground and air vibration, flyrock, and back break, are unavoidable [1][2][3][4][5][6][7]. Among these undesirable effects, ground vibration is the most important effects, since it has adverse effects on the structure of the nearby area, rock mass, and human beings [8,9].…”