在城市建（构）筑物密集区域，准确预测和调控土压平衡盾构施工引起的地面沉降，对保障施工安全、降低环境影响至关重要。为此，提出结合随机森林、遗传算法以及基于拥挤距离的多目标粒子群优化算法，实现盾构施工引起地表沉降预测以及施工参数多目标优化研究。首先，对搜集的盾构工程施工数据进行预处理和相关性分析，建立土压平衡盾构工程施工数据库； 在该数据库的基础上训练随机森林回归模型，采用遗传算法确定最佳超参数组合，获得地表沉降以及推进速度2个智能预测模型。然后，基于对模型输入参数的特征重要度分析，确定8个关键可调施工参数作为待优化参数并建立适应度函数，确定各施工参数约束范围，以最小化沉降及最大化推进速度为目标，采用多目标粒子群算法对土压平衡盾构施工参数进行优化取值。结果表明： 1）建立的地表沉降预测模型R²值为0.937，均方根误差（E_RMS）为11.7 mm，能够得到较为准确的实时预测结果； 2）建立的多目标施工参数优化模型给出了各参数的优化取值范围，优化取值后的地表沉降平均值为-4.28 mm，大幅减小为原参数组合下地表沉降平均值的3.5%左右，且推进速度平均值保持在原推进速度58%的水平。

In urban building areas, accurate prediction and control of surface settlement caused by earth pressure balance (EPB) shield tunneling is essential to construction safety and environmental impacts. Thus, the authors propose a surface settlement prediction method integrating random forest (RF), genetic algorithms (GA), and the multi-objective particle swarm optimization algorithm based on crowding distance (MOPSO-CD) to optimize critical construction parameters. First, data collected from EPB shield tunneling projects are preprocessed and analyzed to establish a comprehensive database. A RF regression model is then trained on this database, with GA used to optimize hyperparameters, resulting in an intelligent surface settlement prediction model. Feature importance analysis identifies construction parameters with significant influence on settlement prediction, reducing the parameter set to eight key factors for constructing fitness functions. Constraints for each parameter are determined, and the MOPSO-CD algorithm is applied to achieve dual optimization objectives: minimizing surface settlement and maximizing driving speed. The proposed settlement prediction model achieves an R² value of up to 0.937 and a root mean square error of 11.7 mm, enabling real-time, accurate surface settlement predictions during shield tunneling. The multi-objective optimization model provides optimal parameter ranges, reducing average surface settlement to －4.28 mm, only 3.5% of the original value, while maintaining an average driving speed at 58% of the initial level.