Modelling the probability of roof fall using digital tools

Abstract

Information on rock mass characteristics, encompassing the overall quality of the rock masses and various geological parameters, is crucial in mining operations. This data guides the selection of mining techniques and the design of roof control methods to minimize the risk of flaking. However, the lithology of roof rocks is often so variable that predicting their behaviour, especially when disturbed by mining activities, is challenging. The study begins with a detailed review of existing literature on roof fall mechanisms and predictive modelling techniques. It was described the dataset used, which includes geological, environmental, and operational parameters collected from various mining sites. This article outlines a methodology for conducting detailed studies of the lithology of coal seam roof rocks in roadways and longwalls as mining progresses. According to the research, forecasting the extent of roof rock fall during the planning stage of mining operations can inform the implementation of appropriate countermeasures to minimize this phenomenon before exploitation begins, thereby significantly reducing ongoing mining costs. Block modelling and stratigraphic (mesh) modelling were employed to predict the volume of rockfall that could contaminate the ore in the analysed mining plot. In the future, this proposed methodology could be used to analyse the progression of roof falls, estimate the mass of falling roof rocks, and calibrate a system for continuous measurement of ore quality in mine workings.