Deep Learning for Intelligent Prediction of Rock Strength by Adopting Measurement While Drilling Data
Publication: International Journal of Geomechanics
Volume 23, Issue 4
Abstract
Precise, rapid, and reliable prediction of rock strength parameters is of great significance for underground engineering. This paper presents a method for predicting rock strength parameters including the Poisson’s ratio (P), elastic modulus (E), and uniaxial compressive strength (UCS) based on computer drilling jumbo measurement while drilling (MWD) data. First, the distribution characteristics and correlation of MWD data are studied; second, a filtering method of MWD data is proposed, which reduces the influence of operational and mechanical factors; finally, an intelligent prediction model of rock mechanics parameters was established, 30 groups of test data were used for application, and the mean absolute percentage error (MAPE) of prediction results for P, E and UCS are 2.11%, 3.11%, and 2.9%, the determination coefficients (R2) are 0.4346, 0.8241, and 0.6616. Compared with the data before optimization, the accuracy of prediction results is improved significantly, it shows that the deep neural network model can accurately predict rock mass parameters.
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Acknowledgments
This research was funded by National Natural Science Foundation of China (Grant No. 51979154 and 52278404), Innovation Team of Shandong Provincial Higher Education Youth Innovation and Technology Program (Grant No. 2021KJG001), State Key Laboratory Open Project of China (Grant No. GJNY-18-73.3), Taishan Scholar Foundation of Shandong Province (Grant No. tsqn202103002), and Shandong University Future Scholars program.
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International Journal of Geomechanics
Volume 23 • Issue 4 • April 2023
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© 2023 American Society of Civil Engineers.
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Received: Jun 6, 2022
Accepted: Oct 25, 2022
Published online: Feb 7, 2023
Published in print: Apr 1, 2023
Discussion open until: Jul 7, 2023
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Cited by
- Xu Cheng, Hua Tang, Zhenjun Wu, Hui Qin, Yonghui Zhang, Measurement While Drilling Method for Estimating the Uniaxial Compressive Strength of Rocks Considering Frictional Dissipation Energy, International Journal of Geomechanics, 10.1061/IJGNAI.GMENG-9877, 24, 11, (2024).