Surrounding Rock Mass Sensing Model Based on TBM Cutterhead Vibration Using WSN-LSTM
[1]GONG Qiuming,LI Shunwen,HUANG Liu,et al.Surrounding Rock Mass Sensing Model Based on TBM Cutterhead Vibration Using WSN-LSTM[J].Journal of Zhengzhou University (Engineering Science),2027,48(XX):1-10.[doi:10.13705/j.issn.1671-6833.2026.04.020]
Copy
Journal of Zhengzhou University (Engineering Science)[ISSN
1671-6833/CN
41-1339/T] Volume:
48
Number of periods:
2027 XX
Page number:
1-10
Column:
Public date:
2027-12-10
- Title:
- Surrounding Rock Mass Sensing Model Based on TBM Cutterhead Vibration Using WSN-LSTM
- Keywords:
- TBM; surrounding rock mass sensing; vibration monitoring system; wavelet scattering network ; deep learning
- CLC:
- U45, TP181
- Abstract:
- To address the limitations of existing surrounding rock mass identification methods based on TBM vibration signals in terms of feature extraction effectiveness and engineering adaptability, a novel surrounding rock mass perception method was proposed integrating wavelet scattering network (WSN) and long short-term memory network (LSTM) using TBM cutterhead vibration data. Firstly, relying on the spiral ramp project of the Beishan Underground Laboratory, a vibration monitoring system was mounted on the TBM cutterhead to acquire vibration signals during the TBM tunneling process. Then, a rock mass sensing database based on cutterhead vibration was established through a series of data preprocessing procedures, including stable tunneling segment extraction, noise reduction, and signal segmentation, combined with the matching of geological information along the tunnel alignment. Secondly, the WSN was employed to perform multi-scale temporal feature extraction from the preprocessed vibration signals, so as to enhance the feature representation capability and noise robustness. On this basis, a WSN-LSTM surrounding rock mass perception model was constructed by leveraging the inherent superiority of the LSTM network in capturing the temporal dependencies. The research results demonstrated that the proposed WSN-LSTM model achieved an accuracy of 93.7% on the test set, which yielded a 5.6 percentage points accuracy improvement compared with the wavelet scattering network-based support vector machine (SVM) model, and outperformed shallow machine learning models (random forest and LightGBM) based on amplitude-domain statistical feature extraction. These findings validated the superiority of WSN in feature extraction from TBM cutterhead vibration signals, as well as the necessity of capturing the temporal dependencies of cutterhead vibration features.
- References:
-
[1] Zhu Mengqi, Zhu Hehua, Wang Xin, et al. Study on CART-based ensemble learning algorithms for predicting TBM tunneling parameters and classing surrounding rock-masses[J]. Chinese Journal of Rock Mechanics and Engineering, 2020, 39(9): 1860-1871. [朱梦琦, 朱合华, 王昕, 等. 基于集成CART算法的TBM掘进参数与围岩等级预测[J]. 岩石力学与工程学报, 2020, 39(9): 1860-1871.]
[2] Erharter G H, Marcher T, Reinhold C. Artificial neural network based online rockmass behavior classification of TBM data[C]//Information Technology in Geo-Engineering. Cham: Springer, 2020: 178-188.
[3] Liu Zaobao, Li Long, Fang Xingli, et al. Hard-rock tunnel lithology prediction with TBM construction big data using a global-attention-mechanism-based LSTM network[J]. Automation in Construction, 2021, 125: 103647.
[4] Zhang Qianli, Liu Zhenyu, Tan Jianrong. Prediction of geological conditions for a tunnel boring machine using big operational data[J]. Automation in Construction, 2019, 100: 73-83.
[5] Mooney M, Walter B, Steele J, et al. Influence of geological conditions on measured TBM vibration frequency[C]//2014 North American Tunneling Conference, NAT 2014. Los Angeles: Society for Mining, Metallurgy and Exploration, 2014: 32-41.
[6] Gong Qiuming, Wu Fan, Wang Dujuan, et al. Development and application of cutterhead working status monitoring system for shield TBM tunnelling[J]. Rock Mechanics and Rock Engineering, 2021, 54(4): 1731-1753.
[7] Li Zonglin, Li Qinglou, Zhang Lilong, et al. Correlation analysis between surrounding rock parameters and mainframe vibration of an open tunnel boring machine[J]. Tunnel Construction, 2024, 44(5): 991-999. [李宗林, 李庆楼, 张立龙, 等. 敞开式TBM隧洞围岩参数与主机振动相关性分析[J]. 隧道建设(中英文), 2024, 44(5): 991-999.]
[8] Yang Yalei, Du Lijie, Li Qingwei, et al. Vibration characteristics of a TBM main beam under different surrounding rocks and tunneling parameters[J]. Journal of Vibration and Shock, 2023, 42(12): 88-97. [杨亚磊, 杜立杰, 李青蔚, 等. 不同围岩与掘进参数下的TBM主梁振动特性[J]. 振动与冲击, 2023, 42(12): 88-97.]
[9] Zhai Shufang, Du Hongkun, Tian Hao, et al. Numerical simulation of TBM three rolling cutters cooperative linear and rotary cutting rock breaking fragmentation[J]. Journal of Zhengzhou University (Engineering Science), 2025, 46(6): 118-126. [翟淑芳, 杜红坤, 田浩, 等. TBM三滚刀协同线性切割和回转切割破岩数值模拟[J]. 郑州大学学报(工学版), 2025, 46(6): 118-126.]
[10] Liu Dongxin, Xiao Yuhang, Zhou Xiaoxiong, et al. Study on vibration characterization parameters of TBM rock-breaking cutterhead[J]. Modern Tunnelling Technology, 2023, 60(4): 153-162. [刘东鑫, 肖宇航, 周小雄, 等. TBM破岩刀盘振动表征参数研究[J]. 现代隧道技术, 2023, 60(4): 153-162.]
[11] Tang Qisheng, Hu Qingsong, Wang Ganggang, et al. Tunnel face rock mass class rapid identification based on TBM cutterhead vibration monitoring and deep learning model[J]. Scientific Reports, 2025, 15: 11563.
[12] Tang Qisheng, Gong Qiuming, Liu Yangyang, et al. Tunnel face rock mass class identification based on multi-domain feature extraction and selection of TBM cutterhead vibration signals[J]. International Journal of Rock Mechanics and Mining Sciences, 2025, 188: 106066.
[13] Wu You, Zhou Xiaoxiong, Cao Yuxin, et al. Ground recognition model of dual-mode shield tunneling based on cutterhead vibration features[J]. Chinese Journal of Underground Space and Engineering, 2024, 20(S2): 629-637. [吴优, 周小雄, 曹玉新, 等. 基于刀盘振动特征的双模盾构掘进地层识别模型[J]. 地下空间与工程学报, 2024, 20(增刊2): 629-637.]
[14] Fang Yingran, Li Xinggao, Liu Hongzhi, et al. Intelligent real-time identification technology of stratum characteristics during slurry TBM tunneling[J]. Tunnelling and Underground Space Technology, 2023, 139: 105216.
[15] Zheng Xiuhe. Research on surrounding rock sensing technology based on TBM cutterhead vibration[D]. Beijing: Beijing Jiaotong University, 2023. [郑修和. 基于TBM刀盘振动的围岩感知技术研究[D]. 北京: 北京交通大学, 2023.]
[16] Liu Mengbo, Liao Shaoming, Yang Yifeng, 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.
[17] Mallat S. Group invariant scattering[J]. Communications on Pure and Applied Mathematics, 2012, 65(10): 1331-1398.
[18] Fan Xin, Cheng Jianyuan, Wang Yunhong, et al. Intelligent recognition of coal mine microseismic signal based on wavelet scattering decomposition transform[J]. Journal of China Coal Society, 2022, 47(7): 2722-2731. [樊鑫, 程建远, 王云宏, 等. 基于小波散射分解变换的煤矿微震信号智能识别[J]. 煤炭学报, 2022, 47(7): 2722-2731.]
[19] Andén J, Mallat S. Deep scattering spectrum[J]. IEEE Transactions on Signal Processing, 2014, 62(16): 4114-4128.
[20] Graves A. Supervised sequence labelling with recurrent neural networks[M]. Berlin, Heidelberg: Springer, 2012: 37-45.
[21] Wang Ju. Progress of geological disposal of high-level radioactive waste in China in the 21st century[J]. Atomic Energy Science and Technology, 2019, 53(10): 2072-2082. [王驹. 中国高放废物地质处置21世纪进展[J]. 原子能科学技术, 2019, 53(10): 2072-2082.]
[22] Sadeghi M, Behnia F, Amiri R. Window selection of the Savitzky-Golay filters for signal recovery from noisy measurements[J]. IEEE Transactions on Instrumentation and Measurement, 2020, 69(8): 5418-5427.
[23] Zhou Xiaoxiong, Gong Qiuming, Yin Lijun, et al. Predicting boring parameters of TBM stable stage based on BLSTM networks combined with attention mechanism[J]. Chinese Journal of Rock Mechanics and Engineering, 2020, 39(S2): 3505-3515. [周小雄, 龚秋明, 殷丽君, 等. 基于BLSTM-AM模型的TBM稳定段掘进参数预测[J]. 岩石力学与工程学报, 2020, 39(增刊2): 3505-3515.]
[24] GB 50487—2008 水利水电工程地质勘察规范[S].
[25] Zhou Yu, Sun Hongyu, Fang Qian, et al. Review of imbalanced data classification methods[J]. Application Research of Computers, 2022, 39(6): 1615-1621. [周玉, 孙红玉, 房倩, 等. 不平衡数据集分类方法研究综述[J]. 计算机应用研究, 2022, 39(6): 1615-1621.]
[26] Sathyanarayanan S. Confusion matrix-based performance evaluation metrics[J]. African Journal of Biomedical Research, 2024: 4023-4031.
[27] Abdullah D M, Abdulazeez A M. Machine learning applications based on SVM classification a review[J]. Qubah-an Academic Journal, 2021, 1(2): 81-90.
- Similar References:
Memo
-
Last Update:
2026-05-25
