针对盾构施工过程中引发的地表沉降问题，通过优化掘进参数实现对地表沉降的控制，保障施工顺利进行。基于长短期记忆神经网络（LSTM）与粒子群算法（PSO）等方法，提出改进LSTM-PSO掘进参数优化模型。首先，构建长短期记忆神经网络模型预测地表沉降，并与随机森林（RF）和BP神经网络的预测结果进行对比，证明LSTM模型的优越性；然后，采用组合权重改进LSTM模型，对比改进前后的地表沉降预测效果；最后，基于改进LSTM地表沉降预测模型，结合粒子群算法，构建改进LSTM-PSO掘进参数优化模型，将其应用于青岛某地铁盾构工程中并验证其可靠性。研究结果表明： 1）LSTM模型在拟合精度和泛化能力方面均比RF模型和BP模型表现出更加优越的性能；采用组合权重改进LSTM模型，改进后的模型对地表沉降的预测性能得到了进一步提升。 2）采用改进LSTM-PSO模型对掘进参数进行优化后，实际施工中地表沉降监测值均在合理范围内，说明所构建的改进LSTM-PSO掘进参数优化模型具有良好的可靠性和工程实用性。
To control the surface settlement caused by shield
tunneling, the tunneling parameters are optimized to guarantee safe
construction. In this study, an improved model for tunneling parameter
optimization based on long and shortterm
memory(LSTM) neural networks and particle swarm optimization(PSO) is proposed.
First, an LSTM neural network model is designed to predict surface settlement,
and its results are compared with those of random forest(RF) and back
propagation(BP) neural networks, demonstrating the superiority of the LSTM
model. Second, the LSTM model is improved via combined weights, and the
predicted surface settlements before and after improvement are compared.
Finally, based on the improved LSTM model and PSO, the improved LSTMPSO model for tunneling parameter optimization is designed and
applied to a metro shield structure project in Qingdao, China, to verify its
reliability. The results reveal that: (1) The LSTM model is superior to the RF
and BP models in terms of both accuracy and its ability for generalization. The
improved LSTM model has a better performance in predicting the surface
settlement then RF and BP models. (2) After optimizing the tunneling parameters
by the improved LSTMPSO model, the surface settlement
is monitored during tunneling and is found to be in a reasonable range, thereby
indicating good feasibility and practicality of this improved LSTMPSO model for tunneling parameter optimization.
