Research for a safer, more productive tomorrow

Better mining systems begin with better questions.

Research, field evidence and practical tools for safer, more productive mechanised mining.

Ojamaa mine · Photo: Kaupo Kikkas / VKG · CC BY-SA 4.0 · cropped

Multidisciplinary research at UP

Mining challenges. Engineering together.

Mechanised mining research at the University of Pretoria brings Mining Engineering and Mechanical and Aeronautical Engineering together, with contributions from Electronic Engineering and other departments. We combine knowledge of rock and mining systems with machine dynamics, sensing, data analysis and control to develop safer, more productive mining systems.

Current research themes

From fundamental questions to practical impact.

We investigate key challenges in mechanised mining through integrated research, field evidence and applied tools. Our themes grow as new programmes develop.

Cutting and drilling hard rock

Understanding rock breakage, cutting processes and tool performance to improve excavation in hard rock.

Illustrative cutting head working at a hard-rock face

Illustrative mining scene

Productivity and maintenance

Improving machine reliability, availability and lifecycle performance through measurement, modelling and field insight.

Illustrative haulage machine in an underground mine

Illustrative mining scene

Utilisation and condition monitoring

Turning machine data into actionable information for safer, more productive mining operations.

LiDAR instrumentation mounted on the actual CMTI research platform

Research instrumentation photograph

Research in motion

Fieldwork, insights and projects from our research.

Selected projects and research insights that connect questions from the field to useful engineering outcomes.

View all projects

Research in the field

Understanding machines where they work.

From instrumented platforms to terrain-aware models, our research connects field measurements with safer, more productive mining.

Explore the research

CMTI research platform with mounted instrumentation

Research project

Traction-aware safety

Combining platform measurements and physical models to understand traction and predict slip.

Actual CMTI platform used in the traction-estimation research

Research insight

From measurements to models

Inside the experiments and algorithms that turn machine data into terrain-aware decisions.

Recorded front roll signal and its filtered measurement from the traction-estimation report

Our research programmes

One home for our mining research.

Shared engineering expertise, brought together through programmes with our funding partners.

Mandela Mining Precinct · SAMERDI

RAMMS research programme

RAMMS seeks to make mechanised mining safer and more productive by connecting rock behaviour, mining operations and equipment performance. Supported by the Mandela Mining Precinct through SAMERDI, the programme investigates cutting and drilling hard rock, productivity and maintenance optimisation, and equipment utilisation and condition monitoring. Laboratory testing, field measurements, physical models and digital twins help turn research into practical insight for equipment selection, maintenance and operation.

Explore the research themes

Epiroc · University of Pretoria

Epiroc Academic and Research Forum

The Epiroc Academic and Research Forum brings UP researchers, students and Epiroc specialists together to improve the safety, productivity and reliability of mining equipment throughout its lifecycle. The programme combines UP's strengths in physical modelling and machine learning to develop hybrid digital twins: digital models that combine engineering knowledge with measured data to understand equipment behaviour and support better decisions.

Collaborative research, specialist training, seminars and technical visits help develop skills and practical solutions for mechanised mining. Research interests include battery electric machines, collision avoidance, underground localisation, drilling and rock support, with digital twins providing a shared approach to performance and predictive maintenance.