Application of Intelligent Rock Bolts for Mining and Critical Evaluation of the Potential, Limitations and Requirements

Rock mass support is widely used in underground mining, and its most common uses include rock bolts, shotcrete, and metal mesh. Traditional support systems, however, lack monitoring capabilities; measurements and monitoring are performed independently. This type of monitoring is primarily discontinuous and requires additional equipment and work hours. Furthermore, visual investigations are confined to monitoring only the surface. The advantage of this system is that it has been in use for a long time, so there are procedures and methods for using it.
Among the common support methods, rock bolts have been the most often utilised support system in underground mining and civil engineering (U.S. Department of Interior Bureau of Mines Staff 1987). By combining existing bolting systems with new, advanced sensors, these systems have the potential to digitalize underground mining. In conclusion, intelligent rock bolts represent a step forward in mining safety and support technology. As the technologies continue to develop, the collaboration between Mining Engineering and Geotechnics will be essential in optimising their use, ensuring that the future of mining is safer and more sustainable.
This thesis examines the fundamental principles of intelligent rock bolting and the possibilities for capturing deformation trends, detecting structural instabilities, and recognising their disadvantages. By combining numerical simulation with real-time data collection from intelligent rock bolts, valuable insights can be gained to optimize support systems. However, current simulations simplify real-world complexities, highlighting the need for further refinement—particularly in determining optimal sensor density, which is critical for capturing expected behaviour and validating the model during numerical modelling.
Despite their many benefits and potential, these systems also present challenges, including higher costs, the need for a reliable power supply, and advanced data analytics. To enable large-scale implementation in mining, further advancements in sensor fusion, model calibration, and cost-efficiency are essential. Nevertheless, these technologies will support the digitalisation of ground support by providing real-time data, reducing the risk of structural failure, and promoting safer, more efficient mining and underground construction practices.