Abstract: To enhance the precision and efficiency of mechanized tunneling in TBM projects, this research investigated a boreability classification method based on tunneling parameters and muck characteristics. Based on a TBM construction section of a tunnel in western Sichuan, tunneling parameters and muck samples were employed as the primary research objects. The time-series data of TBM tunneling parameters were preprocessed using techniques including redundant data elimination, change-point detection and conditional filtering, outlier removal based on boxplot analysis, and smoothing filters. The average total thrust, average cutterhead torque, and average penetration rate per excavation cycle were selected as clustering variables. By employing the silhouette coefficient, BIC(Bayesian information criterion, BIC)and the elbow method, the optimal number of clusters was determined to be four. Subsequently, a Gaussian mixture model(GMM)unsupervised clustering algorithm was applied to classify the tunneling cycles. A boreability classification method was then established by integrating the clustering results with muck morphology characteristics and field geological surveys. The clustering results were validated through subsampling validation and external validation, both of which confirmed a high level of consistency. These findings demonstrated the robustness of the clustering outcomes and the effectiveness of the proposed boreability classification method. This research provides a data-driven method for TBM boreability assessment, offering enhanced guidance for design and improved adaptability in construction.
刘德卫. 基于掘进参数与岩渣特征的TBM隧道围岩可掘性分析[J]. 隧道与地下工程灾害防治, 2026, 8(1): 99-108.
LIU Dewei. TBM tunnel surrounding rock boreability analysis based on excavation parameters and muck characteristics. Hazard Control in Tunnelling and Underground Engineering, 2026, 8(1): 99-108.
[1] 洪开荣. 高强度高磨蚀地层TBM滚刀破岩与磨损研究[J]. 隧道与地下工程灾害防治, 2019, 1(1): 76-85. HONG Kairong. Study on rock breaking and wear of TBM hob in high-strength high-abrasion stratum[J]. Hazard Control in Tunnelling and Underground Engineering, 2019, 1(1): 76-85. [2] 王玉杰, 沈强, 曹瑞琅, 等. 大变形围岩TBM施工适应性分类标准研究[J]. 隧道与地下工程灾害防治, 2020, 2(4): 37-43. WANG Yujie, SHEN Qiang, CAO Ruilang, et al. Study on classification standard of TBM construction adaptability for large deformation surrounding rock[J]. Hazard Control in Tunnelling and Underground Engineering, 2020, 2(4):37-43. [3] ZHANG J M, SHI K B, MAJITI H, et al. Study on the classification and identification methods of surrounding rock excavatability based on the rock-breaking performance of tunnel boring machines[J]. Applied Sciences, 2023, 13(12):7060. [4] 段志伟, 杜立杰, 吕海明, 等. 基于主成分分析与BP神经网络的TBM围岩可掘性分级实时识别方法研究[J]. 隧道建设(中英文), 2020, 40(3): 379-388. DUAN Zhiwei, DU Lijie, LÜ Haiming, et al. Real-time identification method of TBM surrounding rock excavatability grade based on principal component analysis and BP neural network[J]. Tunnel Construction, 2020, 40(3): 379-388. [5] 李建斌, 郑赢豪, 荆留杰, 等. 基于岩体聚类分级的TBM掘进参数预测方法[J]. 岩石力学与工程学报, 2020, 39(增刊2): 3326-3337. LI Jianbin, ZHENG Yinghao, JING Liujie, et al. Prediction method of TBM driving parameters based on rock mass clustering and classification[J]. Chinese Journal of Rock Mechanics and Engineering, 2020, 39(Suppl.2): 3326-3337. [6] WU Z J, WEI R L, CHU Z F, et al. Real-time rock mass condition prediction with TBM tunneling big data using a novel rock-machine mutual feedback perception method[J]. Journal of Rock Mechanics and Geotechnical Engineering, 2021, 13(6): 1311-1325. [7] 李子琛, 朱杰兵, 张宜虎, 等. TBM隧道岩石可掘性评价指标及其应用研究综述[J]. 地下空间与工程学报, 2024, 20(增刊1):508-520. LI Zichen, ZHU Jiebing, ZHANG Yihu, et al. Review on evaluation index of rock drivability in TBM tunnel and its application[J]. Chinese Journal of Underground Space and Engineering, 2024, 20(Suppl.1): 508-520. [8] ACAROGLU O, OZDEMIR L, ASBURY B. A fuzzy logic model to predict specific energy requirement for TBM performance prediction[J]. Tunnelling and Underground Space Technology, 2008, 23(5): 600-608. [9] 宗大超, 赵祎睿, 赵永金. 基于围岩参数的TBM掘进性能预测及应用研究[J]. 人民长江, 2024, 55(8): 161-165. ZONG Dachao, ZHAO Yirui, ZHAO Yongjin. Prediction model of TBM tunneling performance based on surrounding rock parameters and application[J]. Yangtze River, 2024, 55(8): 161-165. [10] 刘佳伟, 张盛, 陈召, 等. 基于TBM掘进性能和适应性分析的围岩分级方法及应用[J]. 煤田地质与勘探, 2023, 51(8): 161-170. LIU Jiawei, ZHANG Sheng, CHEN Zhao, et al. A method for classification of surrounding rock based on the excavatability performance and adaptability of tunnel boring machines and its applications[J]. Coal Geology & Exploration, 2023, 51(8): 161-170. [11] 翟淑芳, 岳奇超, 周小雄, 等. 基于特征粒径的不同刀间距下滚刀最优贯入度分析[J]. 地下空间与工程学报, 2025, 21(4): 1306-1312. ZHAI Shufang, YUE Qichao, ZHOU Xiaoxiong, et al. Optimal penetration analysis of disc cutter under different cutter-spacings based on characteristic particle size[J]. Chinese Journal of Underground Space and Engineering, 2025, 21(4): 1306-1312. [12] 闫长斌, 石雨萱, 李严, 等. 基于岩碴比表面积的完整岩体TBM破岩效率分析[J]. 铁道科学与工程学报, 2025, 22(1): 456-468. YAN Changbin, SHI Yuxuan, LI Yan, et al. Analysis of TBM rock breaking efficiency based on the specific surface area of rock chips for intact rock mass[J]. Journal of Railway Science and Engineering, 2025, 22(1): 456-468. [13] 周小雄, 肖禹航, 龚秋明, 等. 基于图像分析的TBM掘进参数与岩碴特征关系研究[J]. 岩土力学, 2024, 45(4): 1142-1153. ZHOU Xiaoxiong, XIAO Yuhang, GONG Qiuming, et al. Relationships between tunneling parameters of TBM and rock chip characteristics based on image analysis[J]. Rock and Soil Mechanics, 2024, 45(4): 1142-1153. [14] REYNOLDS D A, QUATIERI T F, DUNN R B. Speaker verification using adapted Gaussian mixture models[J]. Digital Signal Processing, 2000, 10(1/2/3): 19-41. [15] YIN X, LIU Q S, HUANG X, et al. Perception model of surrounding rock geological conditions based on TBM operational big data and combined unsupervised-supervised learning[J].Tunnelling and Underground Space Technology, 2022, 120: 104285. [16] SUN M S, CHEN S, HE H F, et al. Classification and prediction of rock mass drillability for a tunnel boring machine based on operational data mining[J]. Frontiers in Earth Science, 2024, 12: 1518844. [17] JAIN A K, MURTY M N, FLYNN P J. Data clustering[J]. ACM Computing Surveys, 1999, 31(3): 264-323. [18] SEBBEH-NEWTON S, AYAWAH P E A, AZURE J W A, et al. Towards TBM automation: on-the-fly characterization and classification of ground conditions ahead of a TBM using data-driven approach[J]. Applied Sciences, 2021, 11(3): 1060. [19] 毛奕喆, 龚国芳, 周星海, 等. 基于马尔可夫过程和深度神经网络的TBM围岩识别[J]. 浙江大学学报(工学版), 2021(3): 448-454. MAO Yizhe, GONG Guofang, ZHOU Xinghai, et al. Identification of TBM surrounding rock based on Markov process and deep neural network[J]. Journal of Zhejiang University(Engineering Science), 2021(3): 448-454. [20] 李蓬喜. 基于机器学习的TBM掘进参数及围岩等级预测研究[D]. 哈尔滨: 哈尔滨工业大学, 2024: 15. LI Pengxi. Research on TBM tunneling parameter and wall rock classification prediction based on machine learning[D]. Harbin:Harbin Institute of Technology, 2024: 15.
2026, Vol. 8 
