Experimental study of rock uniaxial compressive strength prediction with drilling based on vibration signals

被引：0

作者：

Hao J. ^{[1
]}

Liu H. ^{[1
]}

Liu J. ^{[1
]}

Lyu J. ^{[1
]}

Zheng Y. ^{[2
]}

Liu J. ^{[1
]}

机构：

[1] College of Energy and Mining Engineering, Shandong University of Science and Technology, Shandong, Qingdao

[2] Guotun Coal Mine, Shandong Energy Group Luxi Mining Co., Ltd., Shandong, Heze

[3] Academician(Expert) Workstation, Inner Mongolia Shanghaimiao Mining Co., Ltd., Inner Mongolia, Erdos

来源：

Yanshilixue Yu Gongcheng Xuebao/Chinese Journal of Rock Mechanics and Engineering | 2024年 / 43卷 / 06期

基金：

中国国家自然科学基金;

关键词：

artificial neural networks; Fourier transform; rock mechanics; signal noise reduction; uniaxial compressive strength; vibration signals with the drill;

D O I：

10.13722/j.cnki.jrme.2023.0960

中图分类号：

学科分类号：

摘要：

In order to study the response relationship between the vibration signal with drilling and the geomechanical parameters of the rock mass, and to perceive and predict the uniaxial compressive strength of the rock accurately and quickly, a research on the prediction of uniaxial compressive strength of the rock based on the vibration signal with drilling was carried out. Based on indoor drilling experiments of four types of raw rock(coal) specimens, namely granite, limestone, sandstone and coal, the GA-BP neural network model was constructed by combining Fourier transform and vibration signal noise reduction methods, and the prediction performance of the model before and after the noise reduction, as well as the models with different noise reduction methods, were compared and analyzed. The results show that there is a responsive relationship between the vibration signal with drilling and the uniaxial compressive strength of rock, and the uniaxial compressive strength of rock can be predicted by using the vibration signal while drilling. The GA-BP neural network prediction model using Adobe Audition software to denoise the vibration signal has a determination coefficient R2 of 0.838, a root mean square error of 7.063 MPa, and an average absolute error of 5.347 MPa. The results are better than the original prediction model and the general noise reduction method prediction model. Compared with the original prediction model, the prediction accuracy of the optimal noise reduction model is improved by 6.3 %, the root mean square error is reduced by 1.954 MPa, and the average absolute error is reduced by 1.621 MPa. There are some differences in the prediction effect of different lithology in the same prediction model. The GA-BP neural network prediction model of noise reduction signal has excellent prediction ability for uniaxial compressive strength. The method can provide a basis for the measurement of rock mass geomechanical parameters while drilling. © 2024 Academia Sinica. All rights reserved.

引用

页码：1406 / 1424

页数：18

共 36 条

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