DocumentCode :
1802903
Title :
Neural network technology for strata strength characterization
Author :
Utt, Walter K.
Author_Institution :
Res. Lab., Nat. Inst. for Occupational Safety & Health, Spokane, WA, USA
Volume :
6
fYear :
1999
fDate :
36342
Firstpage :
3806
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 :
computerised monitoring; mining; pattern classification; safety systems; self-organising feature maps; structural engineering computing; Spokane Research Laboratory; drill monitoring; neural network; pattern classification; roof bolting; roof drilling; safety; self organising feature map; strata strength; underground mining; Accidents; Data acquisition; Drilling; Fasteners; Health and safety; Injuries; Laboratories; Monitoring; Neural networks; Occupational safety;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 1999. IJCNN '99. International Joint Conference on
Conference_Location :
Washington, DC
ISSN :
1098-7576
Print_ISBN :
0-7803-5529-6
Type :
conf
DOI :
10.1109/IJCNN.1999.830760
Filename :
830760
Link To Document :
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