Prediction Model for Blasting Overbreak and Underbreak based on Drilling Speed of Intelligent Jumbos
DAI Feng-hua;SHI Jing-feng;WANG Shuai-shuai;GAO Xuan;WANG Jiu-yang;YU Li-jie;
Abstract:
Analysis of intelligent jumbo measurement-while-drilling( MWD) parameters reveals that drilling speed stands as the most scientifically valid and operationally accessible indicator for assessing in-situ rock mass conditions prior to tunnel excavation. Analysis of 3732 peripheral holes across 84 blasting rounds from an intelligent jumbo in high-altitude tunneling revealed systematic measurement-while-drilling( MWD) datasets that quantify correlations between penetration rate,explosive parameters,and excavation overbreak/underbreak. The developed analytical framework incorporates four key procedures: identification of pilot drilling phases; clustering analysis of steady-state penetration rates; vectorization and dimensional reduction of charge configurations; all derived by incorporating geometric constraints into the computation of the minimum burden. The extracted parameters comprise peripheral hole extrapolation angles,hole spacing,minimum resistance line,explosive charge configurations,and drilling speed. The MWD characteristics serve as inputs for developing a LightGBM-based excavation-contour prediction system. This computational framework generates quantitative estimates of design-induced overbreak and underbreak dimensions,incorporating early-termination protocols and Bayesian-optimized hyperparameters to enhance model convergence and predictive generalization. Experimental results demonstrate effective model convergence during training,with validation/testing errors maintained within practical engineering tolerances. The model successfully characterizes overbreak-underbreak variations that are correlated with minimum burden,charging configurations,and drilling-speed features. Field implementation achieved significant improvements: average linear overbreak decreased by 46. 8%( from24. 8 cm to 13. 2 cm),and the half-cast factor increased by 49. 1%( from 53% to 79%),demonstrating enhanced contour precision and blast fragmentation quality.
Key Words: drill-blast method;overbreak and underbreak;MWD;drilling speed;machine leaning;LightGBM
Foundation: 山东大学高端工程机械智能制造全国重点实验室开放基金项目(ACMKF2024-07)~~
Authors: DAI Feng-hua;SHI Jing-feng;WANG Shuai-shuai;GAO Xuan;WANG Jiu-yang;YU Li-jie;
References:
- [1]龚伟毅,姚颖康,杜宇翔.中等断面隧道长进尺直孔掏槽爆破开挖与超欠挖控制现场试验[J].爆破,2024,41(2):32-39.[1]GONG Wei-yi,YAO Ying-kang,DU Yu-xiang. Field test of long footage burn cut blasting excavation and control of overcut and undercut in medium section tunnel[J]. Blasting,2024,41(2):32-39.(in Chinese)
- [2]ZHANG Wan-mao,LIU Dun-wen,TANG Yu,et al. Multifractal characteristics of smooth blasting overbreak in extra-long hard rock tunnel[J]. Fractal Fract,2023,7:842.https:∥doi. org/10. 3390/fractalfract7120842.
- [3]WANG Hao-teng,HE Ming-ming. Determining method of tensile strength of rock based on friction characteristics in the drilling process[J]. Rock Mech Rock Eng,2023,56:4211-4227. https:∥doi. org/10. 1007/s00603-023-03276-5.
- [4]WANG Shao-feng,WU Yu-meng,CAI Xin,et al. Strength prediction and drillability identification for rock based on measurement while drilling parameters[J]. J Cent South Univ,2023,30:4036-4051. https:∥doi. org/10. 1007/s11771-023-5492-4.
- [5]LAKSHMINARAYANA C R,TRIPATHI A K,PAL S K.Experimental investigation on potential use of drilling parameters to quantify rock strength[J]. Geo-Engineering,2021,12:23. https:∥doi. org/10. 1186/s40703-021-00152-5.
- [6]LIU Can-can,ZHENG Xi-gui,SHAHANI N M,et al. An experimental investigation into the borehole drilling and strata characteristics[J]. PLo S ONE,2021,16(7):e0253663.
- [7]张树才,仇文革,张齐芳,等.基于凿岩台车钻进参数的岩石强度预测模型研究[J].隧道建设(中英文),2023,43(12):2007-2017.[7]ZHANG Shu-cai,QIU Wen-ge,ZHANG Qi-fang,et al. Research on rock strength prediction model based on drilling parameters of drilling jumbo[J]. Tunnel construction(English and Chinese),2023,43(2):2007-2017.(in Chinese)
- [8]易文豪,王明年,童建军,等.基于支持向量机的大断面岩质隧道掌子面围岩非均一性判识方法[J].中国铁道科学,2021,42(5):112-122.[8]YI Wen-hao,WANG Ming-nian,TONG Jian-jun,et al.Method for identifying the heterogeneity of surrounding rock of large-section rock tunnel face based on support vector machine[J]. China Railway Science,2021,42(5):112-122.(in Chinese)
- [9]王明年,赵思光,童建军,等.基于炮孔钻进参数的隧道掌子面围岩三维精细化分级方法[J].铁道学报,2024,46(10):163-173.[9]WANG Ming-nian,ZHAO Si-guang,TONG Jian-jun,et al. A three-dimensional refined classification method for surrounding rock of tunnel face based on borehole drilling parameters[J]. Journal of the China Railway Society,2024,46(10),163-173.(in Chinese)
- [10]HANSEN T F,LIU Zhong-qiang,TORRESEN J. Predicting rock type from MWD tunnel data using a reproducible ML-modelling process[J]. Tunnelling and Underground Space Technology,2024,152:105843. https:∥doi. org/10. 1016/j. tust. 2024. 105843.
- [11]薛翊国,孔凡猛,杨为民,等.川藏铁路沿线主要不良地质条件与工程地质问题[J].岩石力学与工程学报,2020,39(3):445-468.[11]XUE Yi-guo,KONG Fan-meng,YANG Wei-min,et al.Major adverse geological conditions and engineering geological problems along the Sichuan-Tibet Railway[J].Journal of Rock Mechanics and Engineering,2020,39(3):445-468.(in Chinese)
- [12]RYBAKOVA E O,LIMONOVA E E,NIKOLAEV D P.Fast gaussian filter approximations comparison on SIMD computing platforms[J]. Applied Sciences,2024,14(11):4664. https:∥doi. org/10. 3390/app14114664.
- [13]BELHAOUARI S B. Unsupervised outlier detection in multidimensional data[J]. J Big Data,2021,8:80. https:∥doi. org/10. 1186/s40537-021-00469-z.
- [14]KAREN K,SPIEGELMAN C H. An alternative to ordinary q-q plots:Conditional q-q plots[J]. Computational Statistics&Data Analysis,1986,4(3):167-184. https:∥doi. org/10. 1016/0167-9473(86)90032-0.
- [15]FRALEY C,RAFTERY A E. Model-based clustering,discriminant analysis,and density estimation[J]. Journal of the American Statistical Association,2002,97(458):611-631. https:∥doi. org/10. 1198/016214502760047 131.
- [16]ANDREI A T,GRIGORE O. Low-cost optimized U-Net model with GMM automatic labeling used in forest semantic segmentation[J]. Sensors,2023,23:8991. https:∥doi. org/10. 3390/s23218991.
- [17]MURTAGH F,CONTRERAS P. Algorithms for hierarchical clustering:an overview[J]. WIREs Data Mining Knowl Discov,2012,2:86-97. https:∥doi. org/10.1002/widm. 53.
- [18]赵茉溪,杨玉民,周传波,等.基于MD-PCA-BP模型的露天矿山爆破振动速度预测[J].爆破,2024,41(2):203-211.[18]ZHAO Mo-xi,YANG Yu-min,ZHOU Chuan-bo,et al.Prediction of blasting vibration velocity in open-pit mine based on MD-PCA-BP model[J]. Blasting,2024,41(2):203-211.(in Chinese)
- [19]HE Biao,ARMAGHANI D J,LAI Sai-hin. Assessment of tunnel blasting-induced overbreak:A novel metaheuristic-based random forest approach[J]. Tunnelling and Underground Space Technology,2023,133:104979. https:∥doi. org/10. 1016/j. tust. 2022. 104979.
- [20]LIU Yao-sheng,LI Ang,DAI Feng,et al. An AI-powered approach to improving tunnel blast performance considering geological conditions[J]. Tunnelling and Underground Space Technology,2024,144:105508. https:∥doi. org/10. 1016/j. tust. 2023. 105508.
- DAI Feng-hua
- SHI Jing-feng
- WANG Shuai-shuai
- GAO Xuan
- WANG Jiu-yang
- YU Li-jie
- CCCC Second Highway Engineering Co.
- Ltd.
- Institute of Geotechnical and Underground Engineering
- Shandong University
- DAI Feng-hua
- SHI Jing-feng
- WANG Shuai-shuai
- GAO Xuan
- WANG Jiu-yang
- YU Li-jie
- CCCC Second Highway Engineering Co.
- Ltd.
- Institute of Geotechnical and Underground Engineering
- Shandong University