徐州工程学院学报（自然科学版）2024，Vol.39Issue(4)：11-17,7.
基于时空特征的隧道拱顶沉降预测研究
Tunnel Vault Settlement Prediction Based on Spatiotemporal Characteristics
摘要
Abstract
To address the issue of tunnel vault settlement,a novel settlement prediction model(CTA),combining convolutional neural network(CNN),temporal convolutional network(TCN),and Attention Mechanism,is proposed based on a certain underground excavation tunnel project in Shenzhen.First,the CNN model is employed to extract spatial characteristics of the data,with a view to investigating the impact of different characteristics on settlement.Then,the TCN model captures the temporal characteristics of settlement data to enhance computational efficiency.Finally,the Attention Mechanism captures important temporal node information,thus obtaining prediction results.The CTA model obtains the smallest MAE and RMSE values and the highest R2 value,indicating that the CTA model provides optimal prediction performance and can accurately predict tunnel vault settlement.关键词
暗挖隧道/拱顶沉降/深度学习/注意力机制/时空特征Key words
underground excavation tunnel/vault settlement/deep learning/Attention Mechanism/spatial-temporal characteristics分类
建筑与水利引用本文复制引用
刘健,孙兴凯,陈广库,李尚宣,贾鹏蛟,陈城..基于时空特征的隧道拱顶沉降预测研究[J].徐州工程学院学报（自然科学版）,2024,39(4):11-17,7.基金项目
国家自然科学基金项目(52108380) （52108380）
