  Roof falls due to geological conditions are major safety hazards in mining
and tunneling industries, causing lost work times, injuries, and fatalities.
Several large-opening limestone mines in the Eastern and Midwestern United
States have roof fall problems caused by high horizontal stresses. The typical
hazard management approach for this type of roof fall hazard relies heavily on
visual inspections and expert knowledge. In this study, we propose an
artificial intelligence (AI) based system for the detection roof fall hazards
caused by high horizontal stresses. We use images depicting hazardous and
non-hazardous roof conditions to develop a convolutional neural network for
autonomous detection of hazardous roof conditions. To compensate for limited
input data, we utilize a transfer learning approach. In transfer learning, an
already-trained network is used as a starting point for classification in a
similar domain. Results confirm that this approach works well for classifying
roof conditions as hazardous or safe, achieving a statistical accuracy of 86%.
However, accuracy alone is not enough to ensure a reliable hazard management
system. System constraints and reliability are improved when the features being
used by the network are understood. Therefore, we used a deep learning
interpretation technique called integrated gradients to identify the important
geologic features in each image for prediction. The analysis of integrated
gradients shows that the system mimics expert judgment on roof fall hazard
detection. The system developed in this paper demonstrates the potential of
deep learning in geological hazard management to complement human experts, and
likely to become an essential part of autonomous tunneling operations in those
cases where hazard identification heavily depends on expert knowledge.
