Deep Learning for Intelligent Prediction of Rock Strength by Adopting Measurement While Drilling Data

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) base...

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Bibliographic Details
Published in:International journal of geomechanics 2023-04, Vol.23 (4)
Main Authors: Zhao, Ruijie, Shi, Shaoshuai, Li, Shucai, Guo, Weidong, Zhang, Tao, Li, Xiansen, Lu, Jie
Format: Article
Language:English
Subjects:
Artificial neural networks
Coefficients
Compressive strength
Deep learning
Drilling
Machine learning
Mathematical models
Measurement
Mechanical properties
Modulus of elasticity
Neural networks
Optimization
Parameters
Poisson's ratio
Prediction models
Rock
Rock masses
Rock mechanics
Rocks
Technical Papers
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Summary: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.
ISSN:1532-3641
1943-5622
DOI:10.1061/IJGNAI.GMENG-8080