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.
HU Changming
,
FENG Lili
,
LI Liang
,
YU Xiangtao
,
LI Zengliang
. Surface Settlement Control and Parameter Optimization
of Shield Tunneling Based on Machine Learning[J]. Tunnel Construction, 2023
, 43(12)
: 1985
-1995
.
DOI: 10.3973/j.issn.2096-4498.2023.12.001
