Improved stope design guidelines based on Stability Graph Method for Swedish underground mines

Important results from the project

Yes, the project goals were achieved as planned. Data collection for stability assessment was improved, numerical models were calibrated against field observations, and machine learning was successfully tested for predicting stope stability. In addition, the caving mechanism in the Garpenberg Mine was clarified, and a MATLAB-based method for standardized data analysis was developed, strengthening future modeling and risk management.

Expected long term effects

In the long term, the project is expected to enhance mine safety through improved understanding of caving and stability mechanisms, more efficient data analysis, and more reliable numerical models. The developed tools and guidelines will help reduce dilution, lower costs, and ensure safer operations. The methods can also be applied in other mines, promoting sustainable and competitive mining in Sweden.

Approach and implementation

The project was carried out as planned through field visits, data collection, numerical modeling, and machine learning. The activities were relevant and followed the schedule with minor adjustments. Collaboration with mine engineers worked well and enabled practical testing. No major external obstacles occurred, but some data limitations required methodological adjustments, which ultimately strengthened the project’s applicability and outcomes.