Abstract:
In hard rock excavation, drilling and blasting is commonly used for loosening rock.
Optimum rock fragmentation due to blasting is desirable for downstream operation
productuivity. Environmental impacts due to blasting consist of flyrock, ground
vibration, air over pressure (AOp). Blast performance depends upon mainly 3 factors
consisting of rock mass properties, blast design and explosives system utilised.
Mean fragment size is commonly used for rock fragmentation analysis. During
1960-80, blast performance was evaluated using empirical methods. With
advancement of computing power during the last two decades, various
computentional techniques have been developed for predicting fly rock distance,
peak particle velocity, air over pressure with various input paramters based on set
of blasts. Technique involves training and testing blast data and comparing results
with different computentional algorithm. Various computetntional techniques
consisting of Artifical bee algorithem (ABC), Artifical Neural Network (ANN) , Fuzzy
Interface System (FIS), GA Genetique algorithm (GA), Imperialist Competitive
Alorithm (ICA), Particle Swarm Optimization (PSO), Supoort Vector Machine(
SVM) for predicting blast performance are reviewed. Presently, various
computentional techniques are ustilsed by researchers. This paper further discusses
h ow these techniques can be implemented at operating mines by mining engineers,
blasting team for predicting blast performance.
Citation:
Bhatawdekari, R.M., Danial, J.A., & Edy, T.M. (2018). A review of prediction of blast performance using computational techniques. In A.M.K.B. Abeysinghe & G.V.I. Samaradivakara (Eds.), Proceedings of International Symposium on Earth Resources Management & Environment 2018 (pp. 37-48). Department of Earth Resources Engineering, University of Moratuwa.