基于Attention-ResNet-LSTM混合神经网络的盾构掘进速度预测新方法  

Novel Prediction Method for Shield Advancing Speed Based on Attention-ResNet-Long Short-Term Memory Hybrid Neural

作　　者：高昆[1] 于思淏 许维青[1] 张子新[2] GAO Kun;YU Sihao;XU Weiqing;ZHANG Zixin(The 2nd Engineering Co.,Ltd.of China Railway Tunnel Group,Sanhe 065201,Hebei,China;Department of Geotechnical Engineering,Tongji University,Shanghai 200092,China)

机构地区：[1]中铁隧道集团二处有限公司,河北三河065201 [2]同济大学地下建筑与工程系,上海200092

出　　处：《隧道建设（中英文）》2023年第4期592-601,共10页Tunnel Construction

摘　　要：针对传统方法存在的盾构性能精准预测阻碍盾构快速掘进技术发展的难题,提出一种基于Attention-ResNet-LSTM混合神经网络的盾构掘进速度预测方法。相比传统的LSTM、GRU等网络预测模型,Attention-ResNet-LSTM模型引入了Attention机制。长距离盾构掘进过程中,针对地层条件存在很大的变异性情况,该模型可自适应更新权重矩阵,让模型面对不同的任务时具有一定的自调节能力,可有效提升预测精度。依托中俄东线天然气管道工程对盾构掘进速度进行了实时预测和验证,且结果表明该方法可分析盾构掘进过程中输入、输出参数之间的相关性,且具有较好的适应性。People have always been concerned about the velocity and safety of shield tunneling,which have a close relationship with stratum conditions and operation parameters.However,it is difficult to predict the shield performance using traditional methods due to the complicated shield-ground interaction,impeding the development of the rapid tunneling technology.In this paper,a new prediction method for shield advancing speed based on the Attention-ResNet-Long Short-term Memory(LSTM)hybrid model is proposed to address this problem.Compared with traditional neural networks,such as LSTM and GRU,the proposed model has introduced the Attention mechanism,which can update the weight matrix adaptively according to various stratum conditions during long-distance shield tunneling.As a result,the model can modify the weight matrix itself when confronted with different tasks.This ability effectively increases the prediction accuracy,which has been validated through real-time prediction of shield advancing speed in the eastern route of the China-Russia gas pipeline project.The method can analyze the correlations between input and output parameters during shield tunneling.Moreover,it has good adaptability,which is of great significance in selecting the operation parameters for the velocity and safety of shield tunneling.

关 键 词：盾构隧道 人工智能 混合神经网络 性能预测 掘进速度 

分 类 号：U45[建筑科学—桥梁与隧道工程]