Journal of Shandong University(Engineering Science) ›› 2026, Vol. 56 ›› Issue (5): 102-110.doi: 10.6040/j.issn.1672-3961.0.2025.027

• Civil Engineering • Previous Articles    

Rock strength prediction model based on multi-layer perceptron and drilling parameters of rock drilling jumbo

Peng Liuyi1,2, Liu Hao3, Zhang Qi1,2, Liu Zhihui1,2, Sun Zizheng1,2   

  1. Peng Liuyi1, 2, Liu Hao3, Zhang Qi1, 2, Liu Zhihui1, 2, Sun Zizheng1, 2(1. School of Qilu Transportation, Shandong University, Jinan 250002, Shandong, China;
    2. State Key Laboratory of Intelligent Manufacturing of Advanced Construction Machinery, Jinan 250002, Shandong, China;
    3. Jiangsu XCMG National Key Laboratory &
    Technology Co., Ltd., Xuzhou 221008, Jiangsu, China
  • Published:2026-10-19

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Abstract: To address the problem that existing methods for rapidly obtaining the uniaxial saturated compressive strength(USCS)of rocks based on drilling parameters of rock drilling jumbos rarely considered the influence of impact frequency, leading to insufficient prediction accuracy during the construction of drill-and-blast tunnels, this research conducted experiments on a full-scale rock drilling jumbo, took impact frequency, impact pressure and other parameters as feature variables, and constructed a USCS prediction model for rocks based on a multi-layer perceptron and drilling parameters. The correlation coefficients between drilling parameters and rock strength were used to assign weights to each parameter in the model, which improved the model's accuracy and enabled real-time prediction of the rock USCS. The developed perceptual model relied on easily collected on-site drilling parameters of rock drilling jumbos. Test and verification results showed that the overall prediction accuracy of the model reached 86.67% within an allowable error range of ±3 MPa, indicating high prediction precision.

Key words: drilling parameters, drilling jumbo, impact frequency, multilayer perceptron, strength prediction

CLC Number: 

  • U451+.2
[1] 温森, 吴斐, 李胜, 等. 不同侧压系数和岩石强度下TBM滚刀破岩效率的数值模拟[J]. 隧道与地下工程灾害防治, 2021, 3(4): 9-19. Wen Sen, Wu Fei, Li Sheng, et al. Numerical simulation of rock fragmentation efficiency of TBM disc cutter under different lateral pressure coefficient and rock strength[J]. Hazard Control in Tunnelling and Underground Engi-neering, 2021, 3(4): 9-19.
[2] 姚萌. 基于凿岩台车钻进参数的隧道围岩智能分级方法研究[D]. 成都: 西南交通大学, 2020: 2-3. Yao Meng. Study on intelligent classification of surrounding rock based on drilling jumbo's drilling parameters[D]. Chengdu: Southwest Jiaotong University, 2020: 2-3.
[3] 于浩. 基于MEMS的井眼轨迹随钻测量算法研究[D]. 北京: 中国地质大学(北京), 2019: 3-5. Yu Hao. Research on MWD algorithm for wellbore trajectory based on MEMS[D]. Beijing: China University of Geosciences Beijing, 2019: 3-5.
[4] Kumar B R, 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 Science, 2013, 58:61-72.
[5] Gao H K, Wang Q, Ma F L, et al. The identification method for rock strength and joint while drilling based on cutting energy density[J]. Journal of Central South University, 2024, 31(2): 621-635.
[6] Lakshminarayana C R, Tripathi A K, Pal S K. Prediction of mechanical properties of sedimentary type rocks using rotary drilling parameters[J]. Geotechnical and Geological Engineering, 2020, 38(5): 4863-4876.
[7] 王明年, 易文豪, 赵思光, 等. 基于钻进参数的隧道掌子面围岩单轴抗压强度和弹性模量解析[J]. 中国公路学报, 2024, 37(7):22-34. Wang Mingnian, Yi Wenhao, Zhao Siguang, et al. Analysis of uniaxial compressive strength and elasticity modulus of tunnel face surrounding rock based on drilling parameters[J]. China Journal of Highway and Transport, 2024, 37(7): 22-34.
[8] 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.
[9] 刘河清, 刘建康, 郝建, 等. 基于原岩随钻振动信号的岩石单轴抗压强度快速预测试验研究[J]. 岩土工程学报, 2025, 47(8): 1700-1710. Liu Heqing, Liu Jiankang, Hao Jian, et al. Experimental study on rapid prediction of rock mechanical parameters based on vibration signals of raw rock with drilling[J]. Chinese Journal of Geotechnical Engineering, 2025, 47(8): 1700-1710.
[10] 张树才, 仇文革, 张齐芳, 等. 基于凿岩台车钻进参数的岩石单轴饱和抗压强度预测模型研究[J]. 隧道建设(中英文), 2023, 43(12): 2007-2017. Zhang Shucai, Qiu Wenge, Zhang Qifang, et al. Rock strength prediction model based on drilling parameters of rock drilling jumbo[J]. Tunnel Construction, 2023, 43(12): 2007-2017.
[11] 岳中文, 龙思晨, 闫逸飞, 等. 基于Stacking算法与钻进参数的岩石单轴抗压强度预测[J]. 采矿与安全工程学报, 2026, 43(1): 198-207. Yue Zhongwen, Long Sichen, Yan Yifei, et al. Prediction of uniaxial compressive strength of rock based on stacking algorithm with drilling parameters[J]. Journal of Mining & Safety Engineering, 2026, 43(1): 198-207.
[12] 乐凯. 凿岩台车钻臂定位及破岩过程研究[D]. 武汉: 华中科技大学, 2022: 64-65. Yue Kai. Study on rock drilling arm positioning and rock breaking process[D]. Wuhan: Huazhong University of Science and Technology, 2022: 64-65.
[13] 尹鹏飞. 基于机器学习的隧道围岩分级方法及钻进参数优化研究[D]. 长沙: 中南大学, 2023: 15. Yin Pengfei. Research on tunnel surrounding rock classification and drilling parameters optimization based on machine learning[D]. Changsha: Central South University, 2023: 15.
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