Unconfined compressive strength (UCS) prediction in real-time while drilling using artificial intelligence tools
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Title
Unconfined compressive strength (UCS) prediction in real-time while drilling using artificial intelligence tools
Authors
Keywords
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Journal
NEURAL COMPUTING & APPLICATIONS
Volume -, Issue -, Pages -
Publisher
Springer Science and Business Media LLC
Online
2021-01-01
DOI
10.1007/s00521-020-05546-7
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- (2007) K. Zorlu et al. ENGINEERING GEOLOGY
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