Li Feng, Zeng Huike, Xu Hongbin, Sun Haokai
College of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518060, China.
National Key Laboratory of Green and Long-Life Road Engineering in Extreme Environment (Shenzhen), Shenzhen University, Shenzhen 518060, China.
Sensors (Basel). 2024 Sep 29;24(19):6320. doi: 10.3390/s24196320.
The geological condition monitoring and identification based on TBM-equipped sensors is of great significance for efficient and safe tunnel construction. Full-scale rotary cutting experiments are carried out using tunnel-boring machine disc cutters. Thrust, torque and vibration sensors are equipped on the rotary cutting machine (RCM). A stacking ensemble-learning model for real-time prediction of rock mass classification using features of mathematical statistics is proposed. Three signals, thrust, torque and a novel vibration spectrogram-based local amplification feature, are fed into the model and trained separately. The results show that the stacked ensemble-learning model has better accuracy and stability than any single model, showing a good application prospect in the rock mass classification.
基于配备传感器的隧道掘进机（TBM）进行地质条件监测与识别，对于高效、安全的隧道施工具有重要意义。使用隧道掘进机盘形滚刀进行了全尺寸回转切削试验。在回转切削机（RCM）上配备了推力、扭矩和振动传感器。提出了一种基于数理统计特征的岩体分类实时预测堆叠集成学习模型。将推力、扭矩和一种基于振动频谱图的新型局部放大特征这三个信号输入模型并分别进行训练。结果表明，堆叠集成学习模型比任何单一模型都具有更好的准确性和稳定性，在岩体分类中显示出良好的应用前景。
