Review Article

Review on the Development of Mining Method Selection to Identify New Techniques Using a Cascade-Forward Backpropagation Neural Network

Table 1

Summary of the existing mining method selection (MMS) techniques and main issues associated with them.

Author(s)YearCharacteristicsDrawbacks

Peele, Church1941Uses broad descriptions of thickness, dip, and strength of ore and strength of rockOnly used when there are similar situations in popular methods
Morrison1976The criteria for selecting a mining method are overall descriptors of ore size, type of rock support, and buildup of strain energyThe preference for one method over another is determined by various combinations of ground conditions
Nicholas1981Numerically rates the characteristics of ore deposit based on lithological and geomechanical properties of ore and host rocksThe chosen mining method is the result of combining evaluation and high ranking
Laubscher1981Based on a rock mass classification system that takes into account expected mining effects on rock mass strengthThe preferred method is solely determined by the rock mass classification system
Hartman1987The decision is made based on the lithological and geomechanical characteristics of ore depositsA flow chart must be created to define the mining method
Loubscher1990If the area available for undercutting is large enough, this method can be modified to include the hydraulic radius, making it feasible for more competent rockThe classification must be altered in order to link rock mass rating to hydraulic radius
Nicholas1993Altering the selection procedure by incorporating a weighting factor [28] 
Miller, Pakalnis, and Poulin1995The Nicholas approach has been modified to demonstrate more emphasis on stoping methods, better portraying typical Canadian mining design practicesInsufficient and inadequate for conducting accurate and robust MMS process