- ISSN 0258-2724
- CN 51-1277/U
- EI Compendex
- Scopus
- Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
- Chinese S&T Journal Citation Reports
- Chinese Science Citation Database
| Citation: | JIA Chaojun, CHEN Fanlei, LEI Mingfeng, HUANG Juan, SHI Chenghua, LIU Di. Determination Method of Rock Strength Based on Digital Drilling Parameters[J]. Journal of Southwest Jiaotong University. doi: 10.3969/j.issn.0258-2724.20230328
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Rock strength is a critical parameter for assessing rock stability and safety. Efficient and accurate prediction of rock strength can effectively guide tunnel excavation and support. Digital drilling parameters and mechanical property data of rock were collected from various devices. By analyzing energy transfer during the drilling process, a quantitative relationship between digital drilling parameters and uniaxial compressive strength (UCS) was established. Meanwhile, machine learning methods were employed to develop a rock strength prediction model based on drilling parameters. Four algorithms, including a back-propagation (BP) neural network, random forest, convolutional neural network (CNN), and long short-term memory network were chosen to compare their prediction effects and identify the optimal model. The results indicate that compared to the theoretical formulas and the other three machine learning algorithms, the BP neural network algorithm excels in rock strength prediction, with a root mean square error of 5.794, a mean absolute error of 4.129, and a correlation coefficient of 0.9749.
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