为解决复杂软弱地层中土压平衡盾构掘进速度难以用理论方法预测调控的问题，基于皮尔森相关系数分析了掘进参数与掘进速度的相关性，并提出盾构掘进速度的深度学习预测模型。模型运用粒子群优化算法对BP神经网络的权值与偏置值进行优化，以克服基于梯度下降算法的传统BP神经网络易陷入局部最小值和预测误差大等缺点，预测模型将地质参数与掘进参数作为输入值，盾构掘进速度作为输出值(预测值)。以新建铁路横琴至珠海机场段HJZQ-2标隧道工程为依托，基于贯入度与掘进速度的相关性最高且呈正相关的分析结果，采用监测数据对模型进行训练，利用训练后的深度学习模型对掘进速度进行预测分析。结果显示，具有2层隐藏层的深度学习PSO-BP模型的预测误差基本控制在±4 mm/min(误差在10%以内)，满足实际工程要求，从而验证了模型的有效性与适用性。

The deep learning prediction model of advancing speed is proposed based on the analysis of Pearson correlation coefficient between shield tunneling parameters and advancing speed, so as to solve the problem that the advancing speed of EPB shield in complex soft strata cannot be predicted and controlled by theoretical method. In the proposed model, particle swarm optimization (PSO) algorithm is applied to optimize the weight and bias value of BP network to overcome the shortcomings of traditional BP neural network based on gradient descent algorithm, such as easily falling into local minimum value and large prediction error. The geological and shield tunneling parameters are selected as input values while advancing speed is determined as output. Based on the result that penetration has highest and positive correlation with advancing speed, a case study on a tunnel project of Hengqin to Zhuhai Airport HJZQ2 Bid Section is conducted to check performance of proposed model. The measured data is used to establish advancing speed prediction model. The result displayed that prediction error is basically controlled within ±4 mm/min (error within 10%) which is obtained via PSOBP deep learning model with two hidden layers. The predicted error meets requirement of engineering project which verifies effectiveness and applicability of the proposed model.