郑州大学学报（工学版）2026，Vol.47Issue(4)：17-25,88,10.DOI:10.13705/j.issn.1671-6833.2026.04.020
基于TBM刀盘振动的WSN-LSTM围岩感知模型
Surrounding Rock Mass Sensing Model Based on TBM Cutterhead Vibration Using WSN-LSTM
摘要
Abstract
To address the limitations of existing surrounding rock mass identification methods based on TBM vibra-tion signals in terms of feature extraction effectiveness and engineering adaptability,a novel surrounding rock mass perception method was proposed by integrating wavelet scattering network(WSN)and long short-term memory net-work(LSTM)using TBM cutterhead vibration data.Firstly,relying on the spiral ramp project of the Beishan Un-derground Laboratory,a vibration monitoring system was mounted on the TBM cutterhead to acquire vibration sig-nals during the TBM tunneling process.Then,a rock mass sensing database based on cutterhead vibration was es-tablished through a series of data preprocessing procedures,including stable tunneling segment extraction,noise re-duction,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 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 learn-ing 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 necessi-ty of capturing the temporal dependencies of cutterhead vibration features.关键词
TBM/围岩感知/振动监测系统/小波散射网络/深度学习Key words
TBM/surrounding rock mass sensing/vibration monitoring system/wavelet scattering network/deep learning分类
交通工程引用本文复制引用
龚秋明,李顺文,黄流,王驹,曹子祥,马洪素..基于TBM刀盘振动的WSN-LSTM围岩感知模型[J].郑州大学学报（工学版）,2026,47(4):17-25,88,10.基金项目
国家自然科学基金资助项目(52438005) （52438005）
核设施退役治理专项资助科研项目(科工二司[2020]194号) （科工二司[2020]194号）
