Course content

Mining engineering serves as a common point connecting disciplines like rock engineering, engineering geology, and geotechnical engineering when common operations such as support design, tunnel and slope stability analysis, underground opening dimensioning, rock mass characterization, seismic monitoring, ground reinforcement techniques, subsidence prediction and control, blasting and vibration control, the evaluation and analysis of geotechnical instrumentation & monitoring, and risk assessment and hazard mitigation are considered.

Responsible mining practice, considering United Nations Sustainable Development Goals (UNSDGs), gains increased importance due to the increasing economic competition, social and environmental concerns. Conventionally, empirical methods are widely used in these areas, but may not support responsible mining practices due to local dependency, inherent variability, and high uncertainty in rock properties. Therefore, there is a need for advanced and modern computational tools for modelling and optimisation of mining engineering, rock engineering, engineering geology, and geotechnical engineering operations to assure responsible mining practices for sustainable development.

This course addresses these challenges by introducing advanced computational tools like Machine Learning (ML) techniques essential for modelling and optimising operations in mining engineering and their related disciplines such as rock engineering, engineering geology, and geotechnical engineering. By broadening the scope, the course ensures responsible practices that contribute to sustainable development while being highly relevant to students from various but interconnected fields.

The content will be customised depending on the student's background and nature of the selected project. It is expected that the selected project is related to the student’s PhD thesis and thus the student has enough information on the subject.

Learning outcome

Knowledge:

Knowledge of Machine Learning applications across these interconnected disciplines.

Detailed understanding of the factors affecting the selected operation, including the interaction between these factors and their impact on the outcome.

Awareness of Sustainable Development Goals (UNSDGs) and how they integrate with engineering solutions in mining and related disciplines.

Skills:

Application of ML algorithms for predictive modelling and data mining in various engineering contexts.

Ability to describe the problem and develop model(s) for the considered operation using available software.

Capability to optimise the selected operation considering specific objectives and limitations.

Develop practical skills in using both licensed and open-source computational software and ML tools.

General competence:

Enhanced critical thinking skills to identify the most effective parameters for the selected operation.

Competence in performing modeling and optimization of operations.

Ability to customise and apply course concepts directly to PhD research projects across different disciplines.

Learning methods and activities

The course will begin with group discussions and planning sessions for the selected project work, followed by self-study on the selected project topic. Students will participate in study groups, project work, and practical classes. Analysis of real-world applications of ML and computational modelling to achieve UN Sustainable Development Goals (UNSDGs) will be integral to the learning process.

Specific conditions

Recommended previous knowledge

It is expected that the selected project is related to the student’s PhD thesis.

Required previous knowledge

The course requires admission to the PhD programme Engineering, or approval by the person with course responsibility. The student has to have a Master’s degree in mining engineering or related disciplines. The student should have a broad idea about the selected project subject and dependent and independent variables. The approval will be given by the course coordinator.

Course materials

Given at the start of the semester.