A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
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.
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.
Prediction of blast-induced flyrock in Indian limestone mines using neural networks
R. Trivedi (author) / T.N. Singh (author) / A.K. Raina (author)
2014
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
Prediction of Blast-Induced Flyrock in Opencast Mines Using ANN and ANFIS
| Online Contents | 2015
Prediction of Blast-Induced Flyrock in Opencast Mines Using ANN and ANFIS
| British Library Online Contents | 2015
Prediction of Blast-Induced Flyrock in Opencast Mines Using ANN and ANFIS
| Online Contents | 2015
Studies on Flyrock at Limestone Quarries
| Online Contents | 1999
Studies on Flyrock at Limestone Quarries
| British Library Online Contents | 1999