基于双层LSTM的泥水盾构掘进运行状态监测方法研究与应用

Monitoring Method for Shield Tunneling Operations Based on a Double-Layer Long Short-Term Memory Network

为降低盾构隧道施工过程中对施工人员经验的依赖程度,提高施工的安全性和稳定性,提出基于双层长短期记忆网络(long short-term memory,LSTM)的盾构掘进运行状态监测方法。首先,在建模之前对数据进行预处理,结合极致梯度提升(XGBoost)算法和经验知识,从运行变量中筛选出推力、贯入度、转矩、推进速度等各模块重要的特征变量及其相关变量,以此作为盾构掘进运行状态监测模型的输入,并以盾构关键特征变量作为监测模型的输出;然后,采用深度学习方法,建立双层LSTM故障监测模型,提取环内以及环间的时序关联特征,构建监测统计量T2和Espe,对不同情况下的数据设定不同的监测策略;最后,将模型应用于济南黄河隧道东线工程中,对本文所建立的模型进行验证,并与无时序特征学习的自动编码器模型AE(auto encoder)、单层LSTM模型以及其他算法的监测效果进行比较。研究结果表明:对于正常掘进环,双层LSTM方法的误报率<1.25%;对于掘进状态异常环,双层LSTM的监测准确率达到91.6%,验证了本文方法对于盾构隧道掘进运行状态监测的有效性。

Currently,shield operation monitoring relies heavily on the experience of operators.To overcome this limitation and boost the safety and stability of shield tunneling,a monitoring approach based on a double-layer long short-term memory network(LSTM)is proposed herein.Before modeling,data are preprocessed,and key feature variables,such as penetration and torque,along with their related variables,are selected from the operating data using the extreme gradient boosting algorithm and empirical knowledge.These variables then serve as inputs for the monitoring model,while the key feature variables of the shield serve as the outputs of this model.Next,a double-layer LSTM faultmonitoring model is developed using deep learning techniques to capture temporal correlation features within and between loops.Subsequently,monitoring statistics T2 and E spe are constructed,and different monitoring strategies are applied based on varying situations.Finally,the model is tested on the East Line project of the Yellow river tunnel to validate its effectiveness.Furthermore,the performance of this model is compared with that of an autoencoder model without temporal feature learning,a single-layer LSTM model,and other comparative algorithms.Experimental results reveal that the double-layer LSTM method achieves a false alarm rate of less than 1.25% and a 91.6% detection rate for a normal excavation ring and an abnormal excavation ring,respectively,thus validating its effectiveness in monitoring shield tunneling operations.