为研究泥水盾构掘进参数对地表沉降的影响，依托哈尔滨地铁3号线工程河松—河山叠落区间左线泥水盾构掘进与监测数据，基于遗传算法优化的BP神经网络，针对不同沉降输出形式展开研究，引入隧道距离标签，优化了神经网络拟合效果，并根据此网络模型进行参数敏感性分析，得出3项最敏感参数，并进行穷举试验，进一步分析参数对地表沉降的具体影响效果。研究表明：泥水盾构掘进在穿过某一环2 d后，其地表沉降表现与掘进参数关联性不密切，地表沉降分析可以聚焦于当日监测值；盾构机穿过某一环前、中、后会对该环上方的地表沉降产生不同的影响，后续基于神经网络对地表沉降的研究可考虑纳入该项指标；泥水盾构掘进参数中，降低泥浆黏度和提高泥浆密度可控制地表下沉，提高推进速度可以降低施工对地表沉降的影响。

In order to study the influence of slurry shield tunneling parameters on surface settlement, based on the slurry shield tunneling and monitoring data of the left line of the Hesong-Heshan stacked section of Harbin Metro Line 3 project, based on the BP neural network optimized by genetic algorithm, the different settlement output forms were studied. The tunnel distance label was introduced to optimize the neural network fitting effect, and the parameter sensitivity analysis was carried out according to this network model. Three most sensitive parameters were obtained, and exhaustive tests were carried out to further analyze the specific influence of parameters on surface settlement. The research shows that the surface settlement performance of slurry shield tunneling is not closely related to the tunneling parameters after passing through a certain ring for two days, and the surface settlement analysis can focus on the monitoring value of the day. Before, during and after the shield machine passes through a certain ring, it will have different effects on the surface settlement above the ring. Subsequent research on surface settlement based on neural network can be considered to include this index. Among the parameters of slurry shield tunneling, reducing slurry viscosity and increasing slurry specific gravity can control surface subsidence, and increasing propulsion speed can reduce the impact of construction on surface subsidence.