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Zhao, Ruijie, Shi, Shaoshuai, Li, Shucai, Guo, Weidong, Zhang, Tao, Li, Xiansen, Lu, Jie (2023) Deep Learning for Intelligent Prediction of Rock Strength by Adopting Measurement While Drilling Data. International Journal of Geomechanics, 23 (4) doi:10.1061/ijgnai.gmeng-8080

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Reference TypeJournal (article/letter/editorial)
TitleDeep Learning for Intelligent Prediction of Rock Strength by Adopting Measurement While Drilling Data
JournalInternational Journal of Geomechanics
AuthorsZhao, RuijieAuthor
Shi, ShaoshuaiAuthor
Li, ShucaiAuthor
Guo, WeidongAuthor
Zhang, TaoAuthor
Li, XiansenAuthor
Lu, JieAuthor
Year2023 (April)Volume23
Issue4
PublisherAmerican Society of Civil Engineers (ASCE)
DOIdoi:10.1061/ijgnai.gmeng-8080Search in ResearchGate
Generate Citation Formats
Mindat Ref. ID15693596Long-form Identifiermindat:1:5:15693596:0
GUID0
Full ReferenceZhao, Ruijie, Shi, Shaoshuai, Li, Shucai, Guo, Weidong, Zhang, Tao, Li, Xiansen, Lu, Jie (2023) Deep Learning for Intelligent Prediction of Rock Strength by Adopting Measurement While Drilling Data. International Journal of Geomechanics, 23 (4) doi:10.1061/ijgnai.gmeng-8080
Plain TextZhao, Ruijie, Shi, Shaoshuai, Li, Shucai, Guo, Weidong, Zhang, Tao, Li, Xiansen, Lu, Jie (2023) Deep Learning for Intelligent Prediction of Rock Strength by Adopting Measurement While Drilling Data. International Journal of Geomechanics, 23 (4) doi:10.1061/ijgnai.gmeng-8080
In(2023, April) International Journal of Geomechanics Vol. 23 (4) American Society of Civil Engineers (ASCE)

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Zhao, Ruijie, Shi, Shaoshuai, Yao, Rubing, Yang, Shijie (2024) Application of Relationship model for the Measurement While Drilling Data to Predict Rock Uniaxial Compressive Strength for Tunneling. Rock Mechanics and Rock Engineering, 57 (9). doi:10.1007/s00603-024-03907-5
Lu, Jie, Guo, Weidong, Liu, Jinpei, Zhao, Ruijie, Ding, Yueyang, Shi, Shaoshuai (2023) An Intelligent Advanced Classification Method for Tunnel-Surrounding Rock Mass Based on the Particle Swarm Optimization Least Squares Support Vector Machine. Applied Sciences, 13 (4) 2068 doi:10.3390/app13042068
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