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Neural network technology for strata strength characterization.

Authors
Utt-W
Source
Proceedings of the International Joint Conference on Neural Networks, Mount Royal, New Jersey. Middleton, WI: International Neural Network Society, 1999 Jan; :CD-ROM
Link
NIOSHTIC No.
20024659
Abstract
The process of drilling and bolting the roof is currently one of the most dangerous jobs in underground mining, resulting in about 1,000 accidents with injuries each year in the United States. To increase the safety of underground miners, researchers from the Spokane Research Laboratory of the National Institute for Occupational Safety and Health are applying neural network technology to the classification of mine roof strata in terms of relative strength. In this project, the feasibility of using a monitoring system on a roof drill to assess the integrity of a mine roof and warn a roof drill operator when a weak layer is encountered is being studied. Using measurements taken while a layer is being drilled, one can convert the data to suitably scaled features and classify the strength of the layer with a neural network. The feasibility of using a drill monitoring system to estimate the strength of successive layers of rock was demonstrated in the laboratory.
Keywords
Mining-industry; Underground-mining; Coal-mining; Monitoring-systems; Ground-control; Ground-stability; Engineering-controls; Injury-prevention; Accident-prevention
Publication Date
19990101
Document Type
Conference/Symposia Proceedings
Fiscal Year
1999
NTIS Accession No.
NTIS Price
NIOSH Division
SRL
Source Name
Proceedings of the International Joint Conference on Neural Networks
State
WA
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