Towards Real-time Assessment of Drift Conditions during Potash Mining

The safety of underground mining operations depends heavily on monitoring and management of various hazards that can arise within mines and their supporting infrastructures, particularly the drifts (tunnels). Drifts are critical pathways within mines, and their structural integrity is paramount to ensure the safety of workers and equipment. Traditional methods of evaluating drift stability often involve manual inspection and/or manual collection of data from local instrumentation clusters. This can be time-consuming and limited in scope. The proposed research will explore the use of artificial intelligence (AI) technologies to assess the structural integrity of the drifts in the potash mines. It involves assessing the parameters and criteria required to identify the precursors to failure, as well as the means to collect those parameters in real-time. The findings from this research are expected to offer insights into the implementation of advanced AI solutions for hazard detection, maintenance scheduling and risk mitigation in mines.

Faculty Supervisor:

Christopher Hawkes;Laura Smith

Student:

Partner:

AmbAI

Discipline:

Engineering

Sector:

Professional, scientific and technical services

University:

University of Saskatchewan

Program:

Accelerate

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