Open Cut » Rock Mechanics
The aim of this project was to develop a method for improved prediction of the spatial distribution of rock mass defects and their properties (including fracture frequency) ahead of mining. Current best practice dictates that both slopes and blast patterns are designed for the anticipated geo-structural conditions, but knowledge of these conditions ahead of mining is often limited and based on coarse approximations. However, a valuable amount of information exists in the exposed highwall face of the current strip, if only techniques were available to utilize it.
The project employed statistically calibrated random field techniques to anticipate the geo-structural conditions ahead of mining from the data that is collected from the current mining activity thereby significantly increasing the value of the geo-structural data that is already being collected at mine sites. When historical geological, structural and other characterisation data are acquired for each newly mined highwall strip, a database of the 3-dimensional variation in structural properties (fracture frequency and intensity), that captures local variations and structural complexity, can be acquired and used for improved prediction of geo-structural conditions ahead of mining. A reliable method to extrapolate what is observed into the yet to be mined ground can provide valuable support to production and safety.
The project has investigated the development of a method to predict the most likely structural characteristics, including fracture frequency, and their level of uncertainty, in the next highwall strip, from data obtained from current and previous strips by applying statistical methods based on random field theory. This a technique for generating realistic, reliable, synthetic spatial data from the information contained in measured spatial data.
The project aimed to improve the understanding of the impact of complex joint structures on mine operations by utilising artificial intelligence methods to establish correlations between safety and production related data for prediction and decision making purposes.