Neural Network Technology For Strata Strength Characterization

The National Institute for Occupational Safety and Health (NIOSH)
Walter K. Utt
Organization:
The National Institute for Occupational Safety and Health (NIOSH)
Pages:
4
File Size:
587 KB
Publication Date:

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 froth the Spokane Research Laboratory of- the National Institute for Occupational Safety and Health are applying neural network technology to rite 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.
Citation

APA: Walter K. Utt  Neural Network Technology For Strata Strength Characterization

MLA: Walter K. Utt Neural Network Technology For Strata Strength Characterization. The National Institute for Occupational Safety and Health (NIOSH),

Export
Purchase this Article for $25.00

Create a Guest account to purchase this file
- or -
Log in to your existing Guest account