| Citation: |
LIU Heqing，LIU Jiankang，HAO Jian，et al. Modeling study of uniaxial compressive strength prediction from similar analog drilling test signals[J]. Coal Science and Technology，2025，53（4）：266−279. DOI: 10.12438/cst.2023-1883
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Uniaxial compressive strength (UCS) of rock is one of the important parameters to characterize the properties of rock mass in geotechnical and underground engineering. In addition, in order to perceive and predict the UCS of rock in underground coal mines accurately and quickly, a GA−BP (Genetic Algorithm-Backpropagation) neural network prediction model of UCS based on vibration signals from drilling is constructed based on drilling tests of nine specimens of similar materials with different ratios in the chamber. By varying the number of hidden layers, population and training function of GA−BP neural network, the factors affecting the prediction model and the results are discussed and analyzed to determine the optimal prediction model structure. The results indicate that there is a responding relationship between the vibration signal with drilling and the UCS of similar simulated materials, and the accuracy of the constructed prediction models are above 70%, and the research method of predicting the UCS with the perception of vibration signal with drilling has certain feasibility; The model results are optimal when trainlm is chosen for the training function, the hidden layer is 8, and the number of populations is 20,with coefficients of determination of 0.761 and 0.745 for the training set and the test set, respectively, the root-mean-square errors are 6.039 MPa and 4.254 MPa, and the mean absolute errors are 6.574 MPa and 4.716 MPa, respectively. The UCS prediction method proposed in this paper may provide a new idea for the intelligent identification of rock mechanical properties.
| [1] |
王浩,牟宗龙,易恩兵,等. 基于正态分布置信区间分析法求岩石单轴抗压强度[J]. 煤炭科学技术,2013,41(4):13−15.
WANG Hao,MU Zonglong,YI Enbing,et al. Uniaxial Compressive Strength of Rock Calculated With Confidence Interval Analysis Method Based on Normal Distribution[J]. Coal Science and Technology,2013,41(4):13−15.
|
| [2] |
雷顺,康红普,高富强,等. 破碎煤体点载荷强度测试及单轴抗压强度预测分析[J]. 煤炭科学技术,2019,47(4):107−113.
LEI Shun,KANG Hongpu,GAO Fuqiang,et al. Point load strength test of fragile coal samples and predictive analysis of uniaxial compressive strength[J]. Coal Science and Technology,2019,47(4):107−113.
|
| [3] |
XU C,ZHANG Z L,ZHANG Z T. Prediction of UCS based on multivariate adaptive regression splines and the BP neural network[M]:Deep rock mechanics:From research to engineering. Boca Raton:CRC Press,2018:423−430.
|
| [4] |
MISHRA D A,BASU A. Use of the block punch test to predict the compressive and tensile strengths of rocks[J]. International Journal of Rock Mechanics and Mining Sciences,2012,51:119−127. doi: 10.1016/j.ijrmms.2012.01.016
|
| [5] |
廉玉广,马志超,李江华,等. 岩石单轴加载破坏全过程波速变化特征研究[J]. 煤炭科学技术,2019,47(8):64−69.
LIAN Yuguang,MA Zhichao,LI Jianghua,et al. Study on variation characteristics of wave velocity in whole process of rock uniaxial loading failure[J]. Coal Science and Technology,2019,47(8):64−69.
|
| [6] |
岳中琦,李焯芬,罗锦添,等. 香港大学钻孔过程数字监测仪在土钉加固斜坡工程中的应用[J]. 岩石力学与工程学报,2002,21(11):1685−1690.
YUE Zhongqi,LI Zhuofen,LUO Jintian,et al. Use of hku drilling process monitor in slope stabilization[J]. Chinese Journal of Rock Mechanics and Engineering,2002,21(11):1685−1690.
|
| [7] |
岳中琦. 钻孔过程监测(DPM)对工程岩体质量评价方法的完善与提升[J]. 岩石力学与工程学报,2014,33(10):1977−1996.
YUE Zhongqi. Drilling Process Monitoring for Refining and Upgrading Rock Mass Quality Classification Methods[J]. Chinese Journal of Rock Mechanics and Engineering,2014,33(10):1977−1996.
|
| [8] |
王琦,孙会彬,江贝,等. 基于数字钻探和支持向量机预测岩体单轴抗压强度的方法[J]. 岩土力学,2019,40(3):1221−1228.
WANG Qi,SUN Huibin,JIANG Bei,et al. A method for predicting uniaxial compressive strength of rock mass based on digital drilling test technology and support vector machine[J]. Rock and Soil Mechanics,2019,40(3):1221−1228.
|
| [9] |
王琦,高红科,蒋振华,等. 地下工程围岩数字钻探测试系统研发与应用[J]. 岩石力学与工程学报,2020,39(2):301−310.
WANG Qi,GAO Hongke,JIANG Zhenhua,et al. Development and application of a surrounding rock digital drilling test system of underground engineering[J]. Chinese Journal of Rock Mechanics and Engineering,2020,39(2):301−310.
|
| [10] |
WANG Q,XU S,GAO H K,et al. Energy analysis-based core drilling method for the prediction of rock uniaxial compressive strength[J]. Geomechanics and Engineering,2020,23(1):61−69.
|
| [11] |
XU B,TAN Y C,SUN W B,et al. Study on the prediction of the uniaxial compressive strength of rock based on the SSA-XGBoost model[J]. Sustainability,2023,15(6):5201.
|
| [12] |
KAHRAMAN S,ALBER M,FENER M,et al. The usability of Cerchar abrasivity index for the prediction of UCS and E of Misis Fault Breccia:Regression and artificial neural networks analysis[J]. Expert Systems with Applications,2010,37(12):8750−8756. doi: 10.1016/j.eswa.2010.06.039
|
| [13] |
J K LIU,H J LUAN,Y C ZHANG,et al. Prediction of unconfined compressive strength ahead of tunnel face using measurement-while-drilling data based on hybrid genetic algorithm[J]. Geomechanics and Engineering,2020,22(1):81−95.
|
| [14] |
RAJESH KUMAR B,VARDHAN H,GOVINDARAJ M. Prediction of uniaxial compressive strength,tensile strength and porosity of sedimentary rocks using sound level produced during rotary drilling[J]. Rock Mechanics and Rock Engineering,2011,44(5):613−620.
|
| [15] |
RAJESH KUMAR B,VARDHAN H,GOVINDARAJ M,et al. Regression analysis and ANN models to predict rock properties from sound levels produced during drilling[J]. International Journal of Rock Mechanics and Mining Sciences,2013,58:61−72.
|
| [16] |
ABDELHEDI M,JABBAR R,BEN SAID A,et al. Machine learning for prediction of the uniaxial compressive strength within carbonate rocks[J]. Earth Science Informatics,2023,16(2):1473−1487. doi: 10.1007/s12145-023-00979-9
|
| [17] |
DAVOODI S,MEHRAD M,WOOD D A,et al. Predicting uniaxial compressive strength from drilling variables aided by hybrid machine learning[J]. International Journal of Rock Mechanics and Mining Sciences,2023,170:105546. doi: 10.1016/j.ijrmms.2023.105546
|
| [18] |
MENG W Z,WU W. Machine learning-aided prediction of the mechanical properties of frozen fractured rocks[J]. Rock Mechanics and Rock Engineering,2023,56(1):261−273. doi: 10.1007/s00603-022-03091-4
|
| [19] |
YAGIZ S,SEZER E A,GOKCEOGLU C. Artificial neural networks and nonlinear regression techniques to assess the influence of slake durability cycles on the prediction of uniaxial compressive strength and modulus of elasticity for carbonate rocks[J]. International Journal for Numerical and Analytical Methods in Geomechanics,2012,36(14):1636−1650. doi: 10.1002/nag.1066
|
| [20] |
PISHBIN M,FATHIANPOUR N,MOKHTARI A R. Uniaxial Compressive Strength spatial estimation using different interpolation techniques[J]. International Journal of Rock Mechanics and Mining Sciences,2016,89:136−150. doi: 10.1016/j.ijrmms.2016.09.005
|
| [21] |
DENG H W,DUAN T,TIAN G L,et al. Research on strength prediction model and microscopic analysis of mechanical characteristics of cemented tailings backfill under fractal theory[J]. Minerals,2021,11(8):886. doi: 10.3390/min11080886
|
| [22] |
SABBAĞ N,UYANıK O. Prediction of reinforced concrete strength by ultrasonic velocities[J]. Journal of Applied Geophysics,2017,141:13−23. doi: 10.1016/j.jappgeo.2017.04.005
|
| [23] |
XIA C Y,GUO X D,DAI W T. Numerical test and strength prediction of concrete failure process based on RVM algorithm[J]. Buildings,2022,12(12):2105. doi: 10.3390/buildings12122105
|
| [24] |
MOZUMDER R A,ROY B,LASKAR A I. Support vector regression approach to predict the strength of FRP confined concrete[J]. Arabian Journal for Science and Engineering,2017,42(3):1129−1146. doi: 10.1007/s13369-016-2340-y
|
| [25] |
HUANG L,GAO C,YAN L B,et al. Reliability assessment of confinement models of carbon fiber reinforced polymer-confined concrete[J]. Journal of Reinforced Plastics and Composites,2016,35(12):996−1026. doi: 10.1177/0731684416633899
|
| [26] |
郭书英,马念杰. 岩层破裂状态与钻削机构振动响应特性研究[J]. 采矿与安全工程学报,2016,33(5):911−916.
GUO Shuying,MA Nianjie. Strata fracturing state and vibration response characteristics of drill[J]. Journal of Mining & Safety Engineering,2016,33(5):911−916.
|
| [27] |
刘刚,张家林,刘闯,等. 钻头钻进不同介质时的振动信号特征识别研究[J]. 振动与冲击,2017,36(8):71−78,104.
LIU Gang,ZHANG Jialin,LIU Chuang,et al. An identification method of vibration signal features when bit drills different mediums[J]. Journal of Vibration and Shock,2017,36(8):71−78,104.
|
| [28] |
LIU S W,FU M X,JIA H S,et al. Numerical simulation and analysis of drill rods vibration during roof bolt hole drilling in underground mines[J]. International Journal of Mining Science and Technology,2018,28(6):877−884. doi: 10.1016/j.ijmst.2018.05.018
|
| [29] |
张幼振,张宁,刘璞,等. 典型含煤地层锚固孔钻进动力特性与地层信息识别研究[J]. 煤炭科学技术,2021,49(2):177−185.
ZHANG Youzhen,ZHANG Ning,LIU Pu,et al. Study on drilling dynamic characteristics and stratum information identification of anchor hole in typical coal-bearing stratum[J]. Coal Science and Technology,2021,49(2):177−185.
|
| [30] |
LAZAROVÁ E,KRUĽÁKOVÁ M,KRÚPA V,et al. Regime and rock identification in disintegration by drilling based on vibration signal differentiation[J]. International Journal of Rock Mechanics and Mining Sciences,2022,149:104984. doi: 10.1016/j.ijrmms.2021.104984
|
| [31] |
陈晓君,陈根龙,宋刚,等. 基于岩石性质的钻进振动响应分析[J]. 探矿工程(岩土钻掘工程),2019,46(10):20−26.
CHEN Xiaojun,CHEN Genlong,SONG Gang,et al. Analysis of drilling vibration response based on rock properties[J]. Drilling Engineering,2019,46(10):20−26.
|
| [32] |
KLAIC M,MURAT Z,STAROVESKI T,et al. Tool wear monitoring in rock drilling applications using vibration signals[J]. Wear,2018,408:222−227.
|
| [33] |
YANG Y,ZENG Q L,YIN G J,et al. Vibration test of single coal gangue particle directly impacting the metal plate and the study of coal gangue recognition based on vibration signal and stacking integration[J]. IEEE Access,2019,7:106784−106805. doi: 10.1109/ACCESS.2019.2932118
|
| [34] |
LIU M B,LIAO S M,YANG Y F,et al. Tunnel boring machine vibration-based deep learning for the ground identification of working faces[J]. Journal of Rock Mechanics and Geotechnical Engineering,2021,13(6):1340−1357. doi: 10.1016/j.jrmge.2021.09.004
|
| [35] |
张士科,方宏远,耿勇强. 基于遗传BP神经网络的煤矿爆破振动特征参量预测[J]. 煤炭科学技术,2018,46(9):133−139.
ZHANG Shike,FANG Hongyuan,GENG Yongqiang. Prediction on characteristic parameters of blasting vibration based genetic BP neural network in coal mine[J]. Coal Science and Technology,2018,46(9):133−139.
|
| [36] |
LAWAL A I,KWON S,HAMMED O S,et al. Blast-induced ground vibration prediction in granite Quarries:An application of gene expression programming,ANFIS,and sine cosine algorithm optimized ANN[J]. International Journal of Mining Science and Technology,2021,31(2):265−277. doi: 10.1016/j.ijmst.2021.01.007
|
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