Accurate prediction of
water inrush risk levels in karst tunnels in southwest China is essential to
prevent casualties, construction delays, and economic losses caused by such
disasters. Based on prior research, the factors influencing water inrush in
karst tunnels are identified. A comprehensive evaluation system is constructed
using nine indices: formation lithology, topography, rock formation, rock mass
quality index, rock formation permeability coefficient, sudden water inflow,
groundwater level, annual average precipitation, and tunnel burial depth. A
hybrid deep learning model integrating a convolutional neural network (CNN) and
a long short-term memory (LSTM) network is
proposed. Several karst tunnels in southwest China are used for model training
and validation, and the performance is compared with standalone CNN and LSTM
models. The results show that: (1) The prediction accuracies of the hybrid
model for water inrush risk levels Ⅰ, Ⅱ, Ⅲ, and Ⅳ reach
95%, 85%, 87%, and 91%, respectively. (2) The hybrid model outperforms
traditional CNN and LSTM models in terms of both prediction accuracy and
generalization capability.
HE Junyuan
,
LIANG Di
,
YANG Guihua
,
HE Shiyong
,
LAI Zhongyu
,
ZHANG Chaoyu
,
WANG Wei
,
ZENG Meiting
,
WANG Sheng
. Prediction of Water Inrush
Risk Level Based on a Convolutional Neural Network-Long Short-Term Memory Network Hybrid Model for Karst Tunnels: A Case Study of Wufu Tunnel[J]. Tunnel Construction, 2025
, 45(6)
: 1131
-1141
.
DOI: 10.3973/j.issn.2096-4498.2025.06.008
