关键词: 大直径泥水平衡盾构, 安全稳定性, 倾覆力矩, 俯仰角, 滚动角, 多目标优化, GAT, LSTM, MOMPA, TOPSIS

Abstract: To improve the safety and stability of large-diameter slurry shield tunneling in complex geological conditions, a hybrid intelligent optimization framework that integrates a graph attention network (GAT), long short-term memory network (LSTM), multi-objective marine predators algorithm (MOMPA), and technique for order preference by similarity to ideal solution (TOPSIS) is proposed. The framework enables the prediction and online multiobjective optimization of key shield stability indicators. A multiobjective dynamic coordination strategy was developed for optimizing the overturning moment, pitch angle, and roll angle, thereby supporting the real-time adjustment of shield operating parameters. First, construction data from a large-diameter slurry shield tunnel project in Wuhan were used to establish a database comprising 12 key construction parameters and three safety and stability indicators. The data were processed through cleaning, normalization, and sequence reconstruction. A GAT-LSTM spatiotemporal prediction model was then developed to capture the nonlinear relationships between construction parameters and safety indicators. The model outputs were subsequently used as objective functions in the MOMPA optimization process. MOMPA and TOPSIS were combined to generate a Pareto solution set and identify the preferred set of construction parameters. A rolling-update strategy based on previously optimized parameters was introduced to support online dynamic optimization. Case study results show that (1) the GAT-LSTM model provides accurate predictions of the overturning moment, pitch angle, and roll angle, with test-set R² values of 0.919, 0.923, and 0.976, respectively; (2) the proposed GAT-LSTM-MOMPA-TOPSIS framework reduces the three safety and stability indicators by an average of 18.0%, with reductions of 19.4% in overturning moment, 7.9% in pitch angle, and 26.6% in roll angle; and (3) compared with conventional static optimization methods, the proposed dynamic strategy improves the reported overall performance by approximately 11%.

Key words: large-diameter slurry shield, safety and stability, overturning moment, pitch angle, roll angle, online multiobjective optimization, graph attention network, long short-term memory, multiobjective marine predators algorithm, technique for order preference by similarity to ideal solution