A hybrid deep learning approach for dynamic attitude and position prediction in tunnel construction considering spatio-temporal patterns
作者：
Highlights：
• A novel approach is proposed to predict TBM’s position during construction.
• The spatial and temporal relationships among the TBM parameters are considered.
• A deep-learning framework that incorporates GCN and LSTM is established.
• A realistic tunnel project in Singapore is used to demonstrate as a case study.
• High prediction accuracy is achieved with average R2 of 0.941 for the deviations.
摘要
•A novel approach is proposed to predict TBM’s position during construction.•The spatial and temporal relationships among the TBM parameters are considered.•A deep-learning framework that incorporates GCN and LSTM is established.•A realistic tunnel project in Singapore is used to demonstrate as a case study.•High prediction accuracy is achieved with average R2 of 0.941 for the deviations.
论文关键词：GCN-LSTM,Deep Learning,Tunnel Construction,Real-time prediction
论文评审过程：Received 19 June 2022, Revised 22 August 2022, Accepted 28 August 2022, Available online 5 September 2022, Version of Record 11 September 2022.
论文官网地址：https://doi.org/10.1016/j.eswa.2022.118721
