Machine learning method with heuristic optimization algorithms is proposed to predict the stratum displacement induced by earth pressure balanced shield tunneling. Support vector regression is used as the machine learning method. Four heuristic intelligent optimization algorithms, namely, genetic algorithm, particle swarm optimization, grey wolf optimizer and sparrow search algorithm, are applied to optimize the two hyperparameters of support vector regression model, namely, penalty factor and bandwidth term. Simulated annealing algorithm is introduced to show the necessity of using heuristic algorithms. Mean square error of k-fold cross validation is considered as the fitness function for optimization algorithms. Normalization method and dummy variables are used for data preprocessing. For 115 samples from field measurement, 92 samples are used as the training set, and 23 samples are used as the test set. Three categories of parameters, namely, shield tunneling parameters, tunnel geometrical parameters and stratum types, are used as input parameters for the proposed method. Correlations among parameters are analyzed by Pearson correlation coefficient. The prediction results show that grey wolf optimizer and sparrow search algorithm are suitable methods for determining hyperparameters of support vector regression due to higher accuracy, efficiency, and stability.

提出了基于启发式优化算法的机器学习方法来预测土压平衡盾构掘进引起的地层位移。支持向量回归被用作机器学习方法。采用遗传算法、粒子群优化、灰狼优化器和麻雀搜索算法四种启发式智能优化算法对支持向量回归模型的惩罚因子和带宽项两个超参数进行优化。介绍了模拟退火算法以说明使用启发式算法的必要性。k的均方误差-fold 交叉验证被认为是优化算法的适应度函数。标准化方法和虚拟变量用于数据预处理。对于现场测量的115个样本，92个样本作为训练集，23个样本作为测试集。三类参数，即盾构隧道参数、隧道几何参数和地层类型，被用作该方法的输入参数。通过皮尔逊相关系数分析参数之间的相关性。预测结果表明，灰狼优化器和麻雀搜索算法具有较高的准确性、效率和稳定性，是确定支持向量回归超参数的合适方法。