ABSTRACT

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. 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). Under these circumstances, expert judgement plays an important role, and empirical approaches to design are widely used. Thus, 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 given a brief study on 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/output parameter for the network. The technique of simulation of the result has also been discussed.

1. INTRODUCTION

Safely exploitation of coal has been a big problem since years. In terms of the method of winning coal, the share of opencast mining, which was as low as 14% in 1951, increased to current high level of above 80% whereas the share of underground mining declined from 77% in 1971 to current 20%. Even if, we can't ignore the underground mine coal production due to its good quality of coal as well as for societal reasons. In underground operation ground control problem is an important factor affecting safety, production and efficiency.A view of underground mines with sufficient support and drilling operation have been shown in fig. 1.

(Figure in full paper)

In terms number of mines, out of about 595 operating mines, about 384 are underground mines. In underground coal mining technology, bord and pillar mining method is one of the major technology being used in India, with about 91% of the underground coal production, employing about 57% of total work force. As per statistics of accident data "fall of roof / sides" is one of the major cause of mine accidents. A major consideration in supporting mine roofs is limiting the movement and expansion of the rock strata immediately above the roof. Cause-wise analysis of mine accidents reveals that roof falls continue to remain the single largest killer, As many as 61% of the incidences, which is 28.5% of total fatalities are due to roof fall. Such accidents can be obviated using the accurate measurement and optimization of data and its analysis using Artificial Intelligence. Since artificial intelligence (AI) techniques can make use of heuristic knowledge (rules of thumb) or pattern matching techniques, as opposed to solving a set of mathematical equations, they should be ideally suited for application in the field of geotechnical engineering. Many aspects of mine design are based upon empirical data.

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