为解决盾构掘进过程中参数设定标准不明确、盾构司机主观经验性过强而引发盾构姿态难以控制的工程问题，提出了一种考虑地层条件-隧道结构-掘进参数综合作用的盾构掘进姿态智能预测模型。首先建立了一种自适应权重粒子群优化（adaptive weight particle swarm optimization, AWPSO）算法；然后结合门控循环单元（gated recurrent unit, GRU）神经网络构建盾构姿态预测模型，其中AWPSO算法用于确定GRU神经网络中的最优超参数组合；最后结合上海轨道交通市域线机场联络线张江站-度假区站区间现场监测数据进行了案例验证。结果表明，基于AWPSO-GRU的盾构掘进姿态预测模型具有较高的可靠性和工程实用性，可为盾构掘进过程中施工参数的设定提供参考和依据。

To solve the engineering problem of unclear standards and strong subjective experience when shield tunneling drivers set excavation parameters, which makes it difficult to control the shield tunneling attitude, an intelligent prediction model for shield tunneling attitude that considers the comprehensive effect of geological conditions, tunnel structure, and excavation parameters was proposed. Firstly, AWPSO (adaptive inertia weight particle swarm optimization) algorithm was established. Then, a shield attitude prediction model was constructed by combining GRU (gated recurrent unit) neural network, where the AWPSO algorithm was used to determine the optimal combination of hyperparameters in the GRU neural network. Finally, a case study was conducted to verify the on-site monitoring data between Zhangjiang Station and Resort Station on the Shanghai Suburban Railway Airport Connection Line. The results indicate that the proposed shield tunneling attitude prediction model based on AWPSO-GRU has high reliability and engineering practicality, which can provide reference and basis for setting construction parameters during shield tunneling.