This research is carried out to better understand the nature of the rock mass and to have a better anticipation of rock fragmentation before blasting the rock mass. Current practices of assessing rock mass usually involve techniques that focus on the surface or outcrop of the rock mass such as scanline surveys, window surveys, photogrammetry and laser scanning etc. These techniques generally lack the ability of providing sufficient information about the rock mass as well as bear various inherent constraints such as safety issues, time requirements, user biasness, equipment requirements and reproducibility of results. Similarly, the rock fragmentation is predicted using different mathematical equations known as fragmentation models. However, these models ignore some key factors that significantly affect the nature of fragmentation such as chargeability of blastholes, drilling information e.g. borehole deviation and require numerous rock parameters which are not well known in most cases. These models are often site-specific and are mostly developed for surface mines. Therefore, their application in underground mining is not so common.

The aim of this research is to investigate the possibility of eliminating the constraints and supporting the current practices of rock mass assessment and rock fragmentation prediction. In this regard, drill monitoring technique has been selected as a potential tool for analysing the rock mass and forecast the rock fragmentation.

To test the selected technique, measurement while drilling (MWD) data was collected from three different mines. The variations in MWD data were analysed to identify different zones and structures present inside the rock mass. The results were compared to 3D images obtained by close-range terrestrial digital photogrammetry for validation, which showed a close agreement with each other. Similarly, MWD data was used to classify the rock mass into five different classes i.e. solid, slightly fractured, highly fractured, having cavities, and major cavities in a sublevel caving operation. The loading operation of the blasted rock was filmed and digital images of LHD buckets containing blasted rock were extracted from the video recordings. The blasted rock inside the buckets were categorized as fine, medium, coarse and oversize fragmentation based on their median fragment size (X₅₀). A statistical analysis was carried out to see the correlation between MWD based rock mass classes and fragmentation classes. The results showed that fine and medium size fragmentation has better correlation and can be predicted with higher accuracy using MWD data as compared to coarse and oversize fragmentation.

The results suggest that the drill monitoring technique has the potential to assess rock mass as well as predict rock fragmentation to some extent. It can be used to differentiate between a weak or strong rock mass or between a fractured or competent rock mass. It can be used to differentiate between joints, cavities or foliations etc. It can also be used to predict finer and medium size fractions of the blasted rock with reasonable accuracy. However, the coarser and oversize fragmentation didn’t have a reliable correlation with MWD data. The potential of using drill monitoring technique for rock mass assessment and rock fragment prediction can be further explored and validated using other established rock mass and fragmentation assessment techniques. It can largely overcome the time, cost and safety constraints associated with the methods already in practice.

This research was conducted to evaluate the association of rock fragmentation with different unit operations of a sublevel caving (SLC) production cycle. Drilling, charging, and blasting, as well as the nature of the rock mass, can affect the fragmentation observed at the drawpoints. Similarly, loading the blasted material from the drawpoints and dumping to the orepasses are strongly influenced by the nature of the fragmentation, particularly the oversize rock fragments.

The aim of this research was to evaluate the operational and economic impacts of different fragment sizes in a production cycle of an SLC operation. It also investigated the possibility of predicting rock fragmentation in SLC blasting based on the nature of the rock mass.

The required data for this research were collected from LKAB’s Malmberget iron ore mine. The loading operation of the blasted rock was filmed, and images of Load-Haul-Dump (LHD) buckets containing blasted rock were extracted from the video recordings. The blasted rock inside the buckets was categorized as fine, medium, coarse, and oversize fragmentation, based on the median fragment size (X 50 ). Measurement While Drilling (MWD) data were used to classify the rock mass based on the extent of rock fracturing, and statistical analysis was performed to predict the fragmentation. The results showed that the percentage occurrence of fine and medium fragmentation classes and oversize fragments have better correlations and can be better predicted using MWD data than other fragmentation types. The impact of dumping oversize fragments to orepasses with and without a screening device was evaluated. The results showed an increase in the cycle time of the LHD machines for the orepass with the screening infrastructure.

The results suggest that the drill monitoring technique has the potential to predict rock fragmentation, particularly oversize rock fragments. In addition, the variations in fragmentation during loading should be considered to allocate the best resources for handling different fragment sizes properly and improve density-based ore grade estimations. Grizzlies, along with boulder breakers, should be used to prevent oversize fragments from entering the orepasses and to increase the overall productivity of the operation.