Tunnel Vault Settlement Prediction Based on Spatiotemporal Characteristics
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.
国家科技期刊平台
NSTL
万方数据
