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

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 R² 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 practical references for shield attitude prediction and risk warning in similar engineering projects.