Optimization of Mine Support Parameters Using Neural Network Approach

Since ground control operation is an ‘imprecise’ area of engineering due to the fact that we are dealing with a material produced by nature (the ground). In many circumstances, our fundamental understanding of soil and rock behavior still falls short of being able to predict how the ground will behave. Cause-wise analysis of mine accidents reveals that roof falls continue to remain the single largest killer. Under these circumstances, expert judgement plays an important role, and empirical approaches to design are widely used. Such accidents can be obviated using the accurate measurement, optimization and analysis of data a predictions based on previous results using one of the Artificial Intelligence technique i.e. Neural Networking. It is a simple computational model, which is analogous to that of neural system in human brain. In this paper we have focused Neural Network Technology including Back Propagation Neural Network (BPNN) to train the network for optimization the mine support parameters. Some of the variable parameters associated with the underground excavation work have been taken as input parameter for the network. By simulation the result was compared with the target output until the network error has converged to a threshold minimum.