为解决盾构司机设定施工参数的标准不明确，且盾构司机的数量难以满足盾构隧道建设需求而引发的盾构姿态难以得到良好控制的问题，需要寻找一种可靠且便于推广使用的施工参数-盾构姿态预测模型。基于盾构施工中的大量数据与机器学习中的支持向量机算法，提出一种预测盾构姿态与优化施工参数的方法。所使用的数据采集自上海地铁14号线新建隧道，时间跨度为299 d，具体包括盾构的施工参数以及相关的地层信息。样本集包括43 030个样本，75%的样本用于训练，25%的样本用于测试。测试结果表明模型的拟合优度达到0.863，在15%的容许误差下准确率达到94.5%，远高于传统的拟合方法。将此模型用于施工参数的优化以实现盾构姿态控制，控制结果表明使用此模型优化的施工参数进行掘进时盾构姿态能够得到良好控制。

The standards for shield tunneling parameter setting are not clear, and there are few experienced shield drivers. Accordingly, to control the shield posture when tunneling, a feasible and popular prediction model of shield tunneling parameterposture based on big data of shield tunneling and support vector machine (SVM) in machine learning algorithm is proposed. The collected data consist of 43 030 samples of shield tunneling parameters and associated strata information within 299 days of shield tunneling in new tunnel of Shanghai metro line 14. The 75% of the samples are used for training, while the 25% are used for testing. The testing results show that the goodnessoffit of the SVM algorithm model reaches 0.863, and the accuracy of prediction with 15% tolerance error reaches 94.5%, which is quite promising comparing to the traditional regression analysis. The trained model is finally used for the optimization of construction parameters to control shield posture automatically in an example and turned to be useful for quality control.