Prediction of super-large diameter shield attitude based on LSTM-Transformer

作者：Linfabao Dai, Wen‐Ming Chen, Mingqing Xiao, Wenhao Sun, Zhengzheng Wang · 发表于：Scientific Reports · 年份：2025 · DOI：10.1038/s41598-025-98428-8 · 被引用次数：9 · 研究领域：Inertial Sensor and Navigation、Optical Systems and Laser Technology、Welding Techniques and Residual Stresses

Accurate control of shield tunneling attitude is a critical technology for ensuring construction safety and tunnel quality. With the rapid development of urban underground space, the construction of ultra-large-diameter shield tunnels has become increasingly common. However, the high precision requirements for attitude control and the complex dynamic responses during construction pose significant challenges, which traditional prediction methods struggle to address. To tackle this technical challenge, this study proposes an LSTM-Transformer prediction model that integrates long short term memory (LSTM) networks for temporal feature extraction with Transformer’s global attention mechanism. The model predicts four key shield attitude parameters and is validated using field data from the Jiangyin-Jingjiang Yangtze river tunnel project, with comparative analysis against existing models. The research results show that: (1) The LSTM-Transformer attitude prediction model achieves an R 2 value of 0.881 and a mean absolute error (MAE) value of 2.24 mm, outperforming existing models in prediction accuracy; (2) Feature importance analysis reveals the key parameters that should be prioritized during shield attitude adjustment, providing a theoretical basis for dynamic attitude control; (3) The model effectively provides early warnings for shield attitude deviation risks, significantly enhancing construction safety and efficiency. The research provides important theoretical support and pra...