基于深度学习的盾构姿态预测及纠偏研究
Study on Shield Attitude Prediction and Deflection Correction Based on Deep Learning
桂林 1王飞 1张雯超2
作者信息
- 1. 苏州市轨道交通集团,江苏 苏州 215000
- 2. 南通职业大学 建筑工程学院,江苏 南通 226007
- 折叠
摘要
以苏州某在建隧道工程为研究背景,基于机器学习技术提出一种盾构姿态预测模型和纠偏方法.首先通过卷积神经网络挖掘盾构姿态数据的空间特征,然后通过双向长短期记忆神经网络挖掘数据的时序特征,紧接着通过注意力机制挖掘重要的时间特征信息.在预测结果的基础上,引入Apriori算法对盾构数据的关联规则提取,并提出盾构姿态纠偏方法.实验结果表明该文提出的盾构姿态预测模型具有较好的泛化能力,且相较于选取的 3 种基准模型,得到的均方根误差和平均绝对误差值最小,具有更高的预测精度.基于姿态理论控制模型,构建多环姿态控制模型,实现对姿态调整获取参数建议值,为智能化姿态控制提供参考依据.
Abstract
Taking a tunnel project under construction in Suzhou as the research background,this paper proposes a shield attitude prediction model and correction method based on the machine learning tech-nology.Firstly,the spatial features of shield posture data were mined through a convolutional neural network.Then,the temporal features of data were mined through a bidirectional long short-term memory neural network.Afterwards,the important temporal feature information was mined through the attention mechanism.On the basis of the prediction results,the Apriori algorithm is introduced to extract the as-sociation rules of shield data,and the shield attitude correction method is proposed.Experiments show that the proposed prediction model in this paper has good generalizability.Compared to the three selec-ted baseline models,it achieves the smallest root mean square error and mean absolute error values,in-dicating higher prediction accuracy.Based on the attitude theory control model,a multi-loop attitude control model is constructed to obtain parameter suggestions for attitude adjustment,which provides a theoretical reference for intelligent attitude control.
关键词
盾构隧道/机器学习/姿态预测/纠偏方法/注意力机制Key words
shield tunnelling/machine learning/attitude prediction/correction method/attention mechanism引用本文复制引用
基金项目
国家自然科学基金资助项目(51978430)
中天控股集团技术研发项目(ZTCG-GDJTYJS-JSKF-2021001)
出版年
2024
NETL
NSTL
万方数据