Civil Engineering Faculty Publications
Document Type
Article
Publication Date
4-2026
Abstract
The structural stability of metro systems is essential for safe and reliable urban rail operation. Large-scale underground construction may influence existing metro lines, making accurate settlement prediction necessary. Traditional empirical and numerical methods often fail to capture long-term settlement behavior. This study predicts track bed settlement of Hangzhou Metro Line 1 using monitoring data collected during the Grand Canal diversion construction. A hybrid model (CEEMDAN-BWO-BiLSTM-ATT model) integrating Complete Ensemble Empirical Mode Decomposition with Adaptive Noise, Beluga Whale Optimization, Bidirectional Long Short-Term Memory, and an attention mechanism is developed. Results from four monitoring points along the up line show good performance, with an average R2 of 0.962, RMSE of 0.076 mm, MAE of 0.066 mm, and MAPE of 6.383%. Validation using a down-line monitoring point confirms accuracy and generalization. The results indicate that the model captures nonlinear settlement behavior and provides a reliable data-driven approach for metro deformation prediction.
Recommended Citation
He, Haijie, Zhenlin Wang, Sifan Shen, Shiyu Sheng, Jiang Zhang, Chuang He, Huafeng Shan, Qiongfang Zhang, and Li Ai. "Hybrid Deep Learning Model for Accurate Settlement Forecasting of Metro Tracks under Canal Diversion Engineering." Developments in the Built Environment (2026): 100932. https://doi.org/10.1016/j.dibe.2026.100932
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Publication Title
Developments in the Built Environment
DOI
10.1016/j.dibe.2026.100932
Comments
© 2026 The Authors. Published by Elsevier Ltd. http://creativecommons.org/licenses/by-nc-nd/4.0/