Intelligent self-evacuation path planning for fire emergencies in underground coal mines

In the case of fire emergencies in underground mines, the mine workers undergo significant psychological and physical stress in their battle with time to self-evacuate safely. This can impart their ability to correctly assess the fire-induced hazards in the vicinity of their location and therefore to choose the safest action or the safest escape route. At the same time, the workers do not have any way to know the state of the mine tunnels beyond the immediate vicinity that their senses can provide information about potential hazards. Moreover, the highly dynamic state of a mine, especially under a fire emergency, can render previously safe routes extremely dangerous in the blink of an eye. This study proposes a framework and presents proof of concept for a real-time smart evacuation route-planning approach based on graph theory. In the effort to assist mine workers to safely reach the surface or a refuge chamber, a smart system could provide invaluable acquisition of mine-wide situational awareness to the workers. An IoT of sensors, such as gas concentration, temperature, smoke, oxygen, and air speed sensors, combined with a real-time path planning algorithm could be a powerful tool to such situations. A mine can be represented by a topological map and every location can be assigned a real-time updated value that quantifies the fire-induced hazard based on data collected by a mine-wide IoT. This combinatory risk considers parameters such as concentrations of toxic gases, oxygen levels, heat, and visibility. Safety and health exposure limits as defined from the various regulatory entities are combined with simulated IoT data to calculate the combined risk. The optimized escape routes could significantly assist mine workers to reach a safe location. • Mine-wide real-time data optimize evacuations in underground mines. • Fire hazards data and MSHA criteria are integrated into MCF path-finding algorithms. • Optimized paths minimize exposure to fire-induced hazards. • User-friendly, network flow approach provides reliable evacuation paths. • Proposed mine ventilation evaluation tool can improve mine preparedness.

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Publication Details

Journal
Tunnelling and Underground Space Technology
Published
2025-04-15
DOI
https://doi.org/10.1016/j.tust.2025.106623
Citations
6
Primary Topic
Evacuation and Crowd Dynamics
Type
article
Field-Weighted Citation Impact
6.87

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article

Intelligent self-evacuation path planning for fire emergencies in underground coal mines

Hassan Khaniani, Vasilis Androulakis, Sihua Shao, Mostafa Hassanalian et al.
6 citations
Tunnelling and Underground Space Technology
Evacuation and Crowd Dynamics
6.87
article

Intelligent self-evacuation path planning for fire emergencies in underground coal mines

Hassan Khaniani, Vasilis Androulakis, Sihua Shao, Mostafa Hassanalian, Pedram Roghanchi, Shawn Kingman
article en
6 citations

Abstract

In the case of fire emergencies in underground mines, the mine workers undergo significant psychological and physical stress in their battle with time to self-evacuate safely. This can impart their ability to correctly assess the fire-induced hazards in the vicinity of their location and therefore to choose the safest action or the safest escape route. At the same time, the workers do not have any way to know the state of the mine tunnels beyond the immediate vicinity that their senses can provide information about potential hazards. Moreover, the highly dynamic state of a mine, especially under a fire emergency, can render previously safe routes extremely dangerous in the blink of an eye. This study proposes a framework and presents proof of concept for a real-time smart evacuation route-planning approach based on graph theory. In the effort to assist mine workers to safely reach the surface or a refuge chamber, a smart system could provide invaluable acquisition of mine-wide situational awareness to the workers. An IoT of sensors, such as gas concentration, temperature, smoke, oxygen, and air speed sensors, combined with a real-time path planning algorithm could be a powerful tool to such situations. A mine can be represented by a topological map and every location can be assigned a real-time updated value that quantifies the fire-induced hazard based on data collected by a mine-wide IoT. This combinatory risk considers parameters such as concentrations of toxic gases, oxygen levels, heat, and visibility. Safety and health exposure limits as defined from the various regulatory entities are combined with simulated IoT data to calculate the combined risk. The optimized escape routes could significantly assist mine workers to reach a safe location. • Mine-wide real-time data optimize evacuations in underground mines. • Fire hazards data and MSHA criteria are integrated into MCF path-finding algorithms. • Optimized paths minimize exposure to fire-induced hazards. • User-friendly, network flow approach provides reliable evacuation paths. • Proposed mine ventilation evaluation tool can improve mine preparedness.

Tunnelling and Underground Space TechnologyVol. 162
University of Kentucky (US), Colorado School of Mines (US), New Mexico Institute of Mining and Technology (US)
National Institute for Occupational Safety and Health
Climate action
Openalex Percentile: Top 3%
Evacuation and Crowd Dynamics
6.87
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